Research ArticleA New Energy Management Technique for PVWindGridRenewable Energy System

Onur Ozdal Mengi1 and Ismail Hakki Altas2

1Department of Energy Systems Engineering Engineering Faculty Giresun University 28100 Giresun Turkey2Department of Electrical and Electronics Engineering Engineering Faculty Karadeniz Technical University 61080 Trabzon Turkey

Correspondence should be addressed to Onur Ozdal Mengi onurmengiyahoocom

Received 26 December 2014 Revised 5 February 2015 Accepted 9 February 2015

Academic Editor Elias Stathatos

Copyright copy 2015 O O Mengi and I H Altas This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

An intelligent energy management system (IEMS) for maintaining the energy sustainability in renewable energy systems (RES)is introduced here It consists of wind and photovoltaic (PV) solar panels are established and used to test the proposed IEMSSince the wind and solar sources are not reliable in terms of sustainability and power quality a management system is requiredfor supplying the load power demand The power generated by RES is collected on a common DC bus as a renewable green powerpool to be used for supplying power to loads The renewable DC power bus is operated in a way that there is always a base poweravailable for permanent loadsThen the additional power requirement is supplied from either wind or PV or both depending uponthe availability of these power sources The decision about operating these systems is given by an IEMS with fuzzy logic decisionmaker proposed in this study Using the generated and required power information from the windPV and load sides the fuzzyreasoning based IEMS determines the amount of power to be supplied from each or both sources Besides the IEMS tracks themaximum power operating point of the wind energy system

1 Introduction

In todayrsquos world the increasing need for energy and thefactors such as increasing energy costs limited reservesand environmental pollution leads the renewable energy tobe the most attractive energy source Since these sourceshave unlimited supply and they do not cause environmentalpollution they are studied extensively lately andutilizedmoreand more every day Governments put in new legislationsand feed-in-tariffs to encourage the investors to install newrenewable energy utilization sites [1ndash3] and studies on thistopic are supported by many foundations

Renewable energy sources consist of solar energy windenergy geothermal energy and wave energy which areconsidered to be endless since they exist naturally and theyalways renew themselves [4] It is one of the important topicsthat researchers and scientist work on to obtain energy fromthese sources and use this energy by transforming it into theform of electrical energy

Solar andwind energies have a distinguished place amongthese energy types There are wind and sun everywhere onearth therefore there is more intense study on these sourcesThe aim is not only to obtain the energy but also to turnthe energy to proper values manage the existent energy andterminate the harmonicsWhile managing all these loweringthe cost of the system in every step is taken into considerationToday producing electrical energy from these renewablesources appears to be the main objective [5ndash7]

The combined operation of these systems is far morecomplex than operating them separately In a system withonly solar or wind energy just one element is controlled In ahybrid scheme both sources are controlled individually andsimultaneously depending upon the operating conditionsand energy demand During low sunlight conditions pho-tovoltaic (PV) solar panel cannot supply consistent powerSimilarly wind turbine will not work in conditions withoutwind In this case the required energymust have the structureto make up the lack of energy in conditions when this system

Hindawi Publishing CorporationInternational Journal of PhotoenergyVolume 2015 Article ID 356930 19 pageshttpdxdoiorg1011552015356930

2 International Journal of Photoenergy

does not work regularly or the composition produces lessenergy than the requirement Power management assuresthat the system works efficiently while preventing the lackof energy in loads Here it is aimed at obtaining clean andsustainable energy in stable frequency and definite voltageWhile or after obtaining the energy harmonics must bedefinitely controlled

Power management is important to assure both econom-ical and efficient work of the system in combined usageof renewable energy sources Variable weather conditionsday-night conditions and rapid change in voltages makethis necessary Power management can be achieved by usingmaximum power point tracking (MPPT) [8] devices in orderto determine the most efficient operating point of a system ina particular weather condition and by switching the systemsso that they become active to support each other dynamicallyIt is important to keep the backup batteries full in times whenthere is neither sun nor windWithout backup batteries therewill be no energy in the system In this case for instance itis computerized control mechanismrsquos duty to link the systemto the grid connect the generator or determine and managethe related situations

Nowadays renewable energy sources are structured in twoways as grid connected and standalone Renewable energysources as solar energy and wind energy can be used tofeed loads far from the grid especially the home type onesHowever there are problems in these types of systems whenthere is no sun or wind Users become fully powerless afterthe batteries are flat which are used as backup systems Analternative situation to this is to connect the loads to the gridif they are close to it in conditions that there is no sun orwindand the batteries are empty [9]

In literature review it can be seen that there are manyresearches which include wind turbine and PV solar panelsused together [10 11] and the loads are fed with the energyobtained and power management conducted [12ndash14] Themain aim here is to gain maximum power according toenvironmental conditions andwhether the obtained energy isto be fed by wind energy system (WES) or by PV solar panelssystems according to changing load conditions Similar towind turbines and PV solar panels there are several studiesrelated to energy management and power flow in electricalpower systems and other energy generation units [15ndash17]

Energy sources such as PV solar panels wind turbinesfuel cell and diesel generator can be used both as standaloneor hybrid There are many studies and utilizations suchas windPV [10 11] windfuel cell [18] PVbattery [19]windbattery [20] PVwindfuel cell [21] PVfuel cell [22]PVwindbattery [23] and PVgrid [24] The studies aimto increase power quality ensure energy sustainability andstabilize the amplitude and frequency of the voltage on theload side on a definite value Besides the energymanagementoccupies an important part of the studies related to renew-able energy utilization schemes [13] Energy managementin renewable energy systems deals with both source anduser side control issues to keep the overall system runningsmoothly [25 26]

In addition to this MPPT is one of the important parts ofthe work because IEMS calculates maximum power inWEC

system There are various methods which produce MPPTto obtain maximum power from RES It is tried to run thesystem continuously at this point by defining the maximumpower point with more efficient controls of power electronicsconvertersWhile there are studies for calculation of instanta-neous generated power decided according to measurementsof the environmental conditions studies which focus on theefficient controls are conducted for similarly used engines toproduce maximum power production It is aimed that thewind turbines work withmaximum efficiency [27ndash33] In thisstudy there is an MPPT designed in a different way fromthese methods Here smart control software continuouslyand accurately calculates the maximum power that can beobtained from the wind turbine MPPT is an important partof IEMS Moreover it is different than the other methodswhich include expensive control and measurement methodsin that it is much cheaper and simpler

In this study a power management system will feed theloads from a hybrid power generation system consisting ofPV solar panels WES and grid WES consists of a differentand new MPPT method The hybrid system is connectedto a common DC bus which is used as a power pool forsustainability PV system is also connected to a backupbatteryunit to be charged for emergency usage when additionalpower is needed In addition to the source side the load sidemanagement is also very important for the renewable energysystems and also considered in this study

Besides energy management software can rapidly andcontinuously respond without being bounded to environ-mental conditions which keep continuously some amountof power in reserve and during instantaneous load changescontrol the system efficiently This study is different from theothers in its being efficientmanagement approach and havingdifferent cheaper and simpler peak power point tracking

2 System Description and Methodology

The overall scheme of the proposed hybrid renewable powermanagement system is given in Figure 1 The system consistsof PV and wind power generating units and a utility gridas hybrid electrical sources These three generating units areconnected to a DC power pool over the required convertersA backup battery group is also connected to PV systemin order to store the extra generated solar power when allgenerated power from the PV is not delivered to the loadAC load types are considered in the system and they areconnected to AC power bus which is fed from the commonDC power pool Data collected from source side and loadside is transferred to a computer to be evaluated for decisionmaking process of the power management system

The power management algorithm (PMA) is developedto operate both photovoltaic energy system (PVES) and windenergy system (WES) at the maximum power they can gen-erate under various environmental conditions while main-taining power supply demand of the load side at requiredamount Therefore the power management includes maxi-mum power point tracing of PVES andWES energy storageutility connection and load switching Besides the power

International Journal of Photoenergy 3

AC-DC

DC-DC DC-DC

DC power link

Control datalink

Measurements

Windturbine

Solarpanels

Battery groups

Load 1 Load 2 Load 3 Load n

AC power grid

Charge control

and MPPT

AD

AD

Data acquisition and control

DCAC

AC-DC

DC-DC

ACTransformer

middot middot middot

220V50Hz

Figure 1 Renewable power generation system and energy management

quality issues such as harmonic elimination voltage sagsvoltage increments frequency deviations and voltage mag-nitude are included in the management system It is obviousthat there are too many inputs and parameters into the man-agement decision algorithms A fuzzy logic based decisionmaking algorithm is developed and used for this multi-inputmultiobjective system

21 PV System PVES is modeled using the well-known char-acteristic equation of PV cell The voltage-current equationof a PV cell is based on photocurrent of a P-N junctionsemiconductor The photocurrent is a function of solarirradiation and changes with the sunlight The voltage acrossthe connection terminal of the P-N device varies as a functionof the photocurrentWhen there is a path across the terminalsof the P-N device which is the photovoltaic cell externalcurrent flows through this path if there is a load on thepath then through the load The maximum value of thisexternal current or in other words load current is the shortcircuit current and assumed to be equal to the generatedphotocurrent It is observed that as the cell current increased

the cell gets heated resulting in a decreased terminal voltageTherefore considering this voltage decrement and reversesaturation current of the P-N diode the terminal voltage ofa single PV cell is written as in

119881119862=

119860 times 119896 times 119879119886

119890ln(

119868PH + 119868119878minus 119868119862

119868119878

) minus 119877119878times 119868119862 (1)

119868119862 cell output current (A) 119868PH photocurrent function of

irradiation level and junction of temperature (A) 119868119878 reverse

saturation of current of diode (A)119881119862 cell output voltage (V)

119877119878 series resistance of cell 119890 electron charge (16021917 times

10minus19 C) 119896 Boltzmann constant (1380622 times 10

minus23 J∘K) 119879119886reference cell ambient temperature (∘K) and 119860 curve fittingfactor (100)

PV solar cells are connected in series and parallel com-binations and manufactured as PV modules to be used moreeffectively in commercial applications The voltage of a PVmodule is determined by the PV cells connected in series andthe current of a PV module is determined by the number ofparallel connected series branches The required load power

4 International Journal of Photoenergy

InverterChopperMPPT and battery charge

Photovoltaic panels

Batteryunit

S1

+

I V

Loads

I V

220V50Hz

48V24V

48V DC bus

19sim72V input48V output

42sim60V input

minus

Figure 2 The PV power generation part of the system

is then obtained by connecting the PV modules in series andin parallel yielding the PV arrays [24]

The power flow path from PV power generation unit tothe load is shown in Figure 2 Four PV modules connected 2in series and 2 series branches in parallel are used here to getan array with 42 volts and 5 amperes maximum under goodweather conditions

PV array and battery groups are connected to each otherwith a device including a battery charging regulator andmaximum power point tracker When the sunlight is notsufficient the batteries step in and supply the necessarypower to the loads The MPPT is used to transfer themaximum generated power from the PV array and chargethe batteries if any power is left after feeding the load Thisunit is MOTECH PV4830 MPPT charge controller Thebatteries are also charged from the DC power bus whenthe sunlight is insufficient A battery charge regulator with12243648V and 30A is used as a charging interface deviceTotal peak power generated by the PV array under goodweather conditions is about 320Wp Since the value of thegenerated voltage from the PV array changes depending uponsunlight a DC chopper (19sim72V DC input voltage) is usedto keep the DC voltage from the PV panels at 48V Thischopper is boost converter The diode that is used after thechopper is located in order to protect the chopper It is a kindof electronic fuse It is a diode that prevents reverse currentflow with a high current This is the magnitude of the DCbus voltage which is inverted to 220V 50HzAC voltage by aboost-up inverter In this scheme the current and voltage datafrom the loads aremeasured and transferred to the computerbesides the input voltage and output current of the chopperto be used in decision making process

22 Wind Turbine Emulator and WES The wind turbinesconvert the mechanical energy that is produced by the windto electrical energy To use this electrical energy a voltageand a frequency regulation has been needed The model ofthe wind turbine is developed by the basis of the steady statepower characteristics of the turbine

Calculations of induction machines are completed in 119889-119902axis frame Figure 3 shows the depictions of axis frames

120573

Vcq

Vb

Va

ws

120593r

p120579

120572

d

120595r

120595rd

120595rq

120595r120573

120595r120572

Figure 3 Phase depiction of components of 119889-119902 axis frame in rotorflux vector

Here we can obtain

[

119881119904119889

119881119904119902

] = [

Cos 120579119904

minus Sin 120579119904

Sin 120579119904

Cos 120579119904

][

119881119904120572

119881119904120573

] (2)

After the necessary conversions are completed 119889-119902 axisframe equivalent of the machine is obtained as shown in

[[[[[

[

119881119904119889

119881119904119902

0

0

]]]]]

]

=

[[[[[

[

119877119904

0 0 0

0 119877119904 0 0

0 0 1198771015840

1199030

0 0 0 1198771015840

119903

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

+

[[[[[

[

119871 119904 0 119871119898 0

0 119871119904

0 119871119898

119871119898 0 1198711015840

1199030

0 119871119898

0 1198711015840

119903

]]]]]

]

119889

119889119905

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

International Journal of Photoenergy 5

+

[[[[[

[

0 minus120596119904119871119904

120596119904119871119898

0

120596119904119871 119904 0 0 120596119904119871119898

120596119903119871119898

0 0 minus120596119903119871119903

0 120596119903119871119898 120596119903119871119903 0

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

(3)

119872119890= 119901119871119898(119894119904119902119894119903119889 minus 119894

119904119889119894119903119902) = 119869

1198892120579

1198891199052+ 119861

119889120579

119889119905 (4)

Angular frequency 120596119904is as shown below

120596119904= 120596119903+ 119901120596 (5)

Here 120596119904 represents the angular frequency of stator fluxesand 120596119903 represents rotor fluxes angular frequency and 120596

angular speed of machine shaft also represented by

120596119904=

119889120579

119889119905= 2120587

119899119904

60(6)

and 119899119904is synchrony speed 120596 is shown in

120596 =119889120579

119889119905 (7)

And 119899119904 is as in

119899119904= 60

119891119904

119901 (8)

Here 119891119904 represents the stator flux valueThe relation between flux and current in 119889-119902 axis frame is

as in (9) and (12)

120595119904119889

= 119871119904119894119904119889

+ 119871119898119894119903119889 (9)

120595119904119902 = 119871 119904119894119904119902 + 119871119898119894119903119902 (10)

120595119903119889

= 1198711015840

119903119894119903119889

+ 119871119898119894119904119889

(11)

120595119903119902 = 1198711015840

119903119894119903119902 + 119871119898119894119904119902 (12)

The state-space model according to (0 119889 119902) stator androtor axis frames of the system can be seen in (13) and (17)[34]

119889119894119904119889

119889119905=

1

120590119871119904

[minus119877119864119894119904119889

+ 120590119871119904120596119904119894119904119902

+1198711198981198771015840

119903

11987110158402119903

120595119903119889

+ 119901120596119871119898

1198711015840119903

120595119903119902

+ 119881119904119889]

(13)

119889119894119904119902

119889119905=

1

120590119871 119904

[minus119877119864119894119904119902

+ 120590119871119904120596119904119894119904119889

minus 119901120596119871119898

1198711015840119903

120595119903119889 +1198711198981198771015840

119903

11987110158402119903

120595119903119902 + 119881119904119902]

(14)

119889120595119903119889

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119889 minus1198771015840

119903

1198711015840119903

120595119903119889 + 120596119903120595119903119902 (15)

119889120595119903119902

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119902

minus 120596119903120595119903119889

minus1198771015840

119903

1198711015840119903

120595119903119902

(16)

119872119890= 119901

119871119898

1198711015840119903

(119894119904119902120595119903119889

minus 119894119904119902120595119903119902) = 119869

119889120596

119889119905+ 119861120596 (17)

Consider the following symbols

119871119904 stator winding inductance (H)

119871119903 rotor winding inductance (H)

119872119898 maximummutual inductance between rotor and

stator (H)119877119904 stator phase resistance (Ω)

119877ℎ circle peace resistance between two strips (Ω)

119877c strip resistance (Ω)119872119904119904 converse inductance between stator phase wind-

ings (H)119872119903119903 mutual inductance between rotor strips (H)

120583119900 412058710

minus7119892 air gap (m)119860 air gap segment (m2)119901 number of pole pairs119908119904 stator angular frequency

119908119903 rotor angular frequency

119908 synchronous speed119891119904 stator frequency

120595119904 stator flux vector

120595119903 rotor flux vector

120579 machine axis rotation angle119869 viscosity moment119861 viscosity friction coefficient

TheWES emulator is established by coupling two squirrelcage asynchronous machines together as seen in Figure 4The power of the machine to be used as the prime moverfor representing the wind turbine is selected as 5 kW andthe power of the other machine used as the generator isselected as 3 kW so that the generator can also be operated atoverpower conditions to expand the analysis of the operatingcases

Themachine on the left is a primemover representing thewind turbine and is controlled with a 119881119891 speed controllerDepending on the wind speed the output voltage of thegenerator the secondmachine from the left changes between

6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

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Quantum Chemistry

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Organic Chemistry International

ElectrochemistryInternational Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

2 International Journal of Photoenergy

does not work regularly or the composition produces lessenergy than the requirement Power management assuresthat the system works efficiently while preventing the lackof energy in loads Here it is aimed at obtaining clean andsustainable energy in stable frequency and definite voltageWhile or after obtaining the energy harmonics must bedefinitely controlled

Power management is important to assure both econom-ical and efficient work of the system in combined usageof renewable energy sources Variable weather conditionsday-night conditions and rapid change in voltages makethis necessary Power management can be achieved by usingmaximum power point tracking (MPPT) [8] devices in orderto determine the most efficient operating point of a system ina particular weather condition and by switching the systemsso that they become active to support each other dynamicallyIt is important to keep the backup batteries full in times whenthere is neither sun nor windWithout backup batteries therewill be no energy in the system In this case for instance itis computerized control mechanismrsquos duty to link the systemto the grid connect the generator or determine and managethe related situations

Nowadays renewable energy sources are structured in twoways as grid connected and standalone Renewable energysources as solar energy and wind energy can be used tofeed loads far from the grid especially the home type onesHowever there are problems in these types of systems whenthere is no sun or wind Users become fully powerless afterthe batteries are flat which are used as backup systems Analternative situation to this is to connect the loads to the gridif they are close to it in conditions that there is no sun orwindand the batteries are empty [9]

In literature review it can be seen that there are manyresearches which include wind turbine and PV solar panelsused together [10 11] and the loads are fed with the energyobtained and power management conducted [12ndash14] Themain aim here is to gain maximum power according toenvironmental conditions andwhether the obtained energy isto be fed by wind energy system (WES) or by PV solar panelssystems according to changing load conditions Similar towind turbines and PV solar panels there are several studiesrelated to energy management and power flow in electricalpower systems and other energy generation units [15ndash17]

Energy sources such as PV solar panels wind turbinesfuel cell and diesel generator can be used both as standaloneor hybrid There are many studies and utilizations suchas windPV [10 11] windfuel cell [18] PVbattery [19]windbattery [20] PVwindfuel cell [21] PVfuel cell [22]PVwindbattery [23] and PVgrid [24] The studies aimto increase power quality ensure energy sustainability andstabilize the amplitude and frequency of the voltage on theload side on a definite value Besides the energymanagementoccupies an important part of the studies related to renew-able energy utilization schemes [13] Energy managementin renewable energy systems deals with both source anduser side control issues to keep the overall system runningsmoothly [25 26]

In addition to this MPPT is one of the important parts ofthe work because IEMS calculates maximum power inWEC

system There are various methods which produce MPPTto obtain maximum power from RES It is tried to run thesystem continuously at this point by defining the maximumpower point with more efficient controls of power electronicsconvertersWhile there are studies for calculation of instanta-neous generated power decided according to measurementsof the environmental conditions studies which focus on theefficient controls are conducted for similarly used engines toproduce maximum power production It is aimed that thewind turbines work withmaximum efficiency [27ndash33] In thisstudy there is an MPPT designed in a different way fromthese methods Here smart control software continuouslyand accurately calculates the maximum power that can beobtained from the wind turbine MPPT is an important partof IEMS Moreover it is different than the other methodswhich include expensive control and measurement methodsin that it is much cheaper and simpler

In this study a power management system will feed theloads from a hybrid power generation system consisting ofPV solar panels WES and grid WES consists of a differentand new MPPT method The hybrid system is connectedto a common DC bus which is used as a power pool forsustainability PV system is also connected to a backupbatteryunit to be charged for emergency usage when additionalpower is needed In addition to the source side the load sidemanagement is also very important for the renewable energysystems and also considered in this study

Besides energy management software can rapidly andcontinuously respond without being bounded to environ-mental conditions which keep continuously some amountof power in reserve and during instantaneous load changescontrol the system efficiently This study is different from theothers in its being efficientmanagement approach and havingdifferent cheaper and simpler peak power point tracking

2 System Description and Methodology

The overall scheme of the proposed hybrid renewable powermanagement system is given in Figure 1 The system consistsof PV and wind power generating units and a utility gridas hybrid electrical sources These three generating units areconnected to a DC power pool over the required convertersA backup battery group is also connected to PV systemin order to store the extra generated solar power when allgenerated power from the PV is not delivered to the loadAC load types are considered in the system and they areconnected to AC power bus which is fed from the commonDC power pool Data collected from source side and loadside is transferred to a computer to be evaluated for decisionmaking process of the power management system

The power management algorithm (PMA) is developedto operate both photovoltaic energy system (PVES) and windenergy system (WES) at the maximum power they can gen-erate under various environmental conditions while main-taining power supply demand of the load side at requiredamount Therefore the power management includes maxi-mum power point tracing of PVES andWES energy storageutility connection and load switching Besides the power

International Journal of Photoenergy 3

AC-DC

DC-DC DC-DC

DC power link

Control datalink

Measurements

Windturbine

Solarpanels

Battery groups

Load 1 Load 2 Load 3 Load n

AC power grid

Charge control

and MPPT

AD

AD

Data acquisition and control

DCAC

AC-DC

DC-DC

ACTransformer

middot middot middot

220V50Hz

Figure 1 Renewable power generation system and energy management

quality issues such as harmonic elimination voltage sagsvoltage increments frequency deviations and voltage mag-nitude are included in the management system It is obviousthat there are too many inputs and parameters into the man-agement decision algorithms A fuzzy logic based decisionmaking algorithm is developed and used for this multi-inputmultiobjective system

21 PV System PVES is modeled using the well-known char-acteristic equation of PV cell The voltage-current equationof a PV cell is based on photocurrent of a P-N junctionsemiconductor The photocurrent is a function of solarirradiation and changes with the sunlight The voltage acrossthe connection terminal of the P-N device varies as a functionof the photocurrentWhen there is a path across the terminalsof the P-N device which is the photovoltaic cell externalcurrent flows through this path if there is a load on thepath then through the load The maximum value of thisexternal current or in other words load current is the shortcircuit current and assumed to be equal to the generatedphotocurrent It is observed that as the cell current increased

the cell gets heated resulting in a decreased terminal voltageTherefore considering this voltage decrement and reversesaturation current of the P-N diode the terminal voltage ofa single PV cell is written as in

119881119862=

119860 times 119896 times 119879119886

119890ln(

119868PH + 119868119878minus 119868119862

119868119878

) minus 119877119878times 119868119862 (1)

119868119862 cell output current (A) 119868PH photocurrent function of

irradiation level and junction of temperature (A) 119868119878 reverse

saturation of current of diode (A)119881119862 cell output voltage (V)

119877119878 series resistance of cell 119890 electron charge (16021917 times

10minus19 C) 119896 Boltzmann constant (1380622 times 10

minus23 J∘K) 119879119886reference cell ambient temperature (∘K) and 119860 curve fittingfactor (100)

PV solar cells are connected in series and parallel com-binations and manufactured as PV modules to be used moreeffectively in commercial applications The voltage of a PVmodule is determined by the PV cells connected in series andthe current of a PV module is determined by the number ofparallel connected series branches The required load power

4 International Journal of Photoenergy

InverterChopperMPPT and battery charge

Photovoltaic panels

Batteryunit

S1

+

I V

Loads

I V

220V50Hz

48V24V

48V DC bus

19sim72V input48V output

42sim60V input

minus

Figure 2 The PV power generation part of the system

is then obtained by connecting the PV modules in series andin parallel yielding the PV arrays [24]

The power flow path from PV power generation unit tothe load is shown in Figure 2 Four PV modules connected 2in series and 2 series branches in parallel are used here to getan array with 42 volts and 5 amperes maximum under goodweather conditions

PV array and battery groups are connected to each otherwith a device including a battery charging regulator andmaximum power point tracker When the sunlight is notsufficient the batteries step in and supply the necessarypower to the loads The MPPT is used to transfer themaximum generated power from the PV array and chargethe batteries if any power is left after feeding the load Thisunit is MOTECH PV4830 MPPT charge controller Thebatteries are also charged from the DC power bus whenthe sunlight is insufficient A battery charge regulator with12243648V and 30A is used as a charging interface deviceTotal peak power generated by the PV array under goodweather conditions is about 320Wp Since the value of thegenerated voltage from the PV array changes depending uponsunlight a DC chopper (19sim72V DC input voltage) is usedto keep the DC voltage from the PV panels at 48V Thischopper is boost converter The diode that is used after thechopper is located in order to protect the chopper It is a kindof electronic fuse It is a diode that prevents reverse currentflow with a high current This is the magnitude of the DCbus voltage which is inverted to 220V 50HzAC voltage by aboost-up inverter In this scheme the current and voltage datafrom the loads aremeasured and transferred to the computerbesides the input voltage and output current of the chopperto be used in decision making process

22 Wind Turbine Emulator and WES The wind turbinesconvert the mechanical energy that is produced by the windto electrical energy To use this electrical energy a voltageand a frequency regulation has been needed The model ofthe wind turbine is developed by the basis of the steady statepower characteristics of the turbine

Calculations of induction machines are completed in 119889-119902axis frame Figure 3 shows the depictions of axis frames

120573

Vcq

Vb

Va

ws

120593r

p120579

120572

d

120595r

120595rd

120595rq

120595r120573

120595r120572

Figure 3 Phase depiction of components of 119889-119902 axis frame in rotorflux vector

Here we can obtain

[

119881119904119889

119881119904119902

] = [

Cos 120579119904

minus Sin 120579119904

Sin 120579119904

Cos 120579119904

][

119881119904120572

119881119904120573

] (2)

After the necessary conversions are completed 119889-119902 axisframe equivalent of the machine is obtained as shown in

[[[[[

[

119881119904119889

119881119904119902

0

0

]]]]]

]

=

[[[[[

[

119877119904

0 0 0

0 119877119904 0 0

0 0 1198771015840

1199030

0 0 0 1198771015840

119903

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

+

[[[[[

[

119871 119904 0 119871119898 0

0 119871119904

0 119871119898

119871119898 0 1198711015840

1199030

0 119871119898

0 1198711015840

119903

]]]]]

]

119889

119889119905

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

International Journal of Photoenergy 5

+

[[[[[

[

0 minus120596119904119871119904

120596119904119871119898

0

120596119904119871 119904 0 0 120596119904119871119898

120596119903119871119898

0 0 minus120596119903119871119903

0 120596119903119871119898 120596119903119871119903 0

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

(3)

119872119890= 119901119871119898(119894119904119902119894119903119889 minus 119894

119904119889119894119903119902) = 119869

1198892120579

1198891199052+ 119861

119889120579

119889119905 (4)

Angular frequency 120596119904is as shown below

120596119904= 120596119903+ 119901120596 (5)

Here 120596119904 represents the angular frequency of stator fluxesand 120596119903 represents rotor fluxes angular frequency and 120596

angular speed of machine shaft also represented by

120596119904=

119889120579

119889119905= 2120587

119899119904

60(6)

and 119899119904is synchrony speed 120596 is shown in

120596 =119889120579

119889119905 (7)

And 119899119904 is as in

119899119904= 60

119891119904

119901 (8)

Here 119891119904 represents the stator flux valueThe relation between flux and current in 119889-119902 axis frame is

as in (9) and (12)

120595119904119889

= 119871119904119894119904119889

+ 119871119898119894119903119889 (9)

120595119904119902 = 119871 119904119894119904119902 + 119871119898119894119903119902 (10)

120595119903119889

= 1198711015840

119903119894119903119889

+ 119871119898119894119904119889

(11)

120595119903119902 = 1198711015840

119903119894119903119902 + 119871119898119894119904119902 (12)

The state-space model according to (0 119889 119902) stator androtor axis frames of the system can be seen in (13) and (17)[34]

119889119894119904119889

119889119905=

1

120590119871119904

[minus119877119864119894119904119889

+ 120590119871119904120596119904119894119904119902

+1198711198981198771015840

119903

11987110158402119903

120595119903119889

+ 119901120596119871119898

1198711015840119903

120595119903119902

+ 119881119904119889]

(13)

119889119894119904119902

119889119905=

1

120590119871 119904

[minus119877119864119894119904119902

+ 120590119871119904120596119904119894119904119889

minus 119901120596119871119898

1198711015840119903

120595119903119889 +1198711198981198771015840

119903

11987110158402119903

120595119903119902 + 119881119904119902]

(14)

119889120595119903119889

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119889 minus1198771015840

119903

1198711015840119903

120595119903119889 + 120596119903120595119903119902 (15)

119889120595119903119902

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119902

minus 120596119903120595119903119889

minus1198771015840

119903

1198711015840119903

120595119903119902

(16)

119872119890= 119901

119871119898

1198711015840119903

(119894119904119902120595119903119889

minus 119894119904119902120595119903119902) = 119869

119889120596

119889119905+ 119861120596 (17)

Consider the following symbols

119871119904 stator winding inductance (H)

119871119903 rotor winding inductance (H)

119872119898 maximummutual inductance between rotor and

stator (H)119877119904 stator phase resistance (Ω)

119877ℎ circle peace resistance between two strips (Ω)

119877c strip resistance (Ω)119872119904119904 converse inductance between stator phase wind-

ings (H)119872119903119903 mutual inductance between rotor strips (H)

120583119900 412058710

minus7119892 air gap (m)119860 air gap segment (m2)119901 number of pole pairs119908119904 stator angular frequency

119908119903 rotor angular frequency

119908 synchronous speed119891119904 stator frequency

120595119904 stator flux vector

120595119903 rotor flux vector

120579 machine axis rotation angle119869 viscosity moment119861 viscosity friction coefficient

TheWES emulator is established by coupling two squirrelcage asynchronous machines together as seen in Figure 4The power of the machine to be used as the prime moverfor representing the wind turbine is selected as 5 kW andthe power of the other machine used as the generator isselected as 3 kW so that the generator can also be operated atoverpower conditions to expand the analysis of the operatingcases

Themachine on the left is a primemover representing thewind turbine and is controlled with a 119881119891 speed controllerDepending on the wind speed the output voltage of thegenerator the secondmachine from the left changes between

6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 3

AC-DC

DC-DC DC-DC

DC power link

Control datalink

Measurements

Windturbine

Solarpanels

Battery groups

Load 1 Load 2 Load 3 Load n

AC power grid

Charge control

and MPPT

AD

AD

Data acquisition and control

DCAC

AC-DC

DC-DC

ACTransformer

middot middot middot

220V50Hz

Figure 1 Renewable power generation system and energy management

quality issues such as harmonic elimination voltage sagsvoltage increments frequency deviations and voltage mag-nitude are included in the management system It is obviousthat there are too many inputs and parameters into the man-agement decision algorithms A fuzzy logic based decisionmaking algorithm is developed and used for this multi-inputmultiobjective system

21 PV System PVES is modeled using the well-known char-acteristic equation of PV cell The voltage-current equationof a PV cell is based on photocurrent of a P-N junctionsemiconductor The photocurrent is a function of solarirradiation and changes with the sunlight The voltage acrossthe connection terminal of the P-N device varies as a functionof the photocurrentWhen there is a path across the terminalsof the P-N device which is the photovoltaic cell externalcurrent flows through this path if there is a load on thepath then through the load The maximum value of thisexternal current or in other words load current is the shortcircuit current and assumed to be equal to the generatedphotocurrent It is observed that as the cell current increased

the cell gets heated resulting in a decreased terminal voltageTherefore considering this voltage decrement and reversesaturation current of the P-N diode the terminal voltage ofa single PV cell is written as in

119881119862=

119860 times 119896 times 119879119886

119890ln(

119868PH + 119868119878minus 119868119862

119868119878

) minus 119877119878times 119868119862 (1)

119868119862 cell output current (A) 119868PH photocurrent function of

irradiation level and junction of temperature (A) 119868119878 reverse

saturation of current of diode (A)119881119862 cell output voltage (V)

119877119878 series resistance of cell 119890 electron charge (16021917 times

10minus19 C) 119896 Boltzmann constant (1380622 times 10

minus23 J∘K) 119879119886reference cell ambient temperature (∘K) and 119860 curve fittingfactor (100)

PV solar cells are connected in series and parallel com-binations and manufactured as PV modules to be used moreeffectively in commercial applications The voltage of a PVmodule is determined by the PV cells connected in series andthe current of a PV module is determined by the number ofparallel connected series branches The required load power

4 International Journal of Photoenergy

InverterChopperMPPT and battery charge

Photovoltaic panels

Batteryunit

S1

+

I V

Loads

I V

220V50Hz

48V24V

48V DC bus

19sim72V input48V output

42sim60V input

minus

Figure 2 The PV power generation part of the system

is then obtained by connecting the PV modules in series andin parallel yielding the PV arrays [24]

The power flow path from PV power generation unit tothe load is shown in Figure 2 Four PV modules connected 2in series and 2 series branches in parallel are used here to getan array with 42 volts and 5 amperes maximum under goodweather conditions

PV array and battery groups are connected to each otherwith a device including a battery charging regulator andmaximum power point tracker When the sunlight is notsufficient the batteries step in and supply the necessarypower to the loads The MPPT is used to transfer themaximum generated power from the PV array and chargethe batteries if any power is left after feeding the load Thisunit is MOTECH PV4830 MPPT charge controller Thebatteries are also charged from the DC power bus whenthe sunlight is insufficient A battery charge regulator with12243648V and 30A is used as a charging interface deviceTotal peak power generated by the PV array under goodweather conditions is about 320Wp Since the value of thegenerated voltage from the PV array changes depending uponsunlight a DC chopper (19sim72V DC input voltage) is usedto keep the DC voltage from the PV panels at 48V Thischopper is boost converter The diode that is used after thechopper is located in order to protect the chopper It is a kindof electronic fuse It is a diode that prevents reverse currentflow with a high current This is the magnitude of the DCbus voltage which is inverted to 220V 50HzAC voltage by aboost-up inverter In this scheme the current and voltage datafrom the loads aremeasured and transferred to the computerbesides the input voltage and output current of the chopperto be used in decision making process

22 Wind Turbine Emulator and WES The wind turbinesconvert the mechanical energy that is produced by the windto electrical energy To use this electrical energy a voltageand a frequency regulation has been needed The model ofthe wind turbine is developed by the basis of the steady statepower characteristics of the turbine

Calculations of induction machines are completed in 119889-119902axis frame Figure 3 shows the depictions of axis frames

120573

Vcq

Vb

Va

ws

120593r

p120579

120572

d

120595r

120595rd

120595rq

120595r120573

120595r120572

Figure 3 Phase depiction of components of 119889-119902 axis frame in rotorflux vector

Here we can obtain

[

119881119904119889

119881119904119902

] = [

Cos 120579119904

minus Sin 120579119904

Sin 120579119904

Cos 120579119904

][

119881119904120572

119881119904120573

] (2)

After the necessary conversions are completed 119889-119902 axisframe equivalent of the machine is obtained as shown in

[[[[[

[

119881119904119889

119881119904119902

0

0

]]]]]

]

=

[[[[[

[

119877119904

0 0 0

0 119877119904 0 0

0 0 1198771015840

1199030

0 0 0 1198771015840

119903

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

+

[[[[[

[

119871 119904 0 119871119898 0

0 119871119904

0 119871119898

119871119898 0 1198711015840

1199030

0 119871119898

0 1198711015840

119903

]]]]]

]

119889

119889119905

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

International Journal of Photoenergy 5

+

[[[[[

[

0 minus120596119904119871119904

120596119904119871119898

0

120596119904119871 119904 0 0 120596119904119871119898

120596119903119871119898

0 0 minus120596119903119871119903

0 120596119903119871119898 120596119903119871119903 0

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

(3)

119872119890= 119901119871119898(119894119904119902119894119903119889 minus 119894

119904119889119894119903119902) = 119869

1198892120579

1198891199052+ 119861

119889120579

119889119905 (4)

Angular frequency 120596119904is as shown below

120596119904= 120596119903+ 119901120596 (5)

Here 120596119904 represents the angular frequency of stator fluxesand 120596119903 represents rotor fluxes angular frequency and 120596

angular speed of machine shaft also represented by

120596119904=

119889120579

119889119905= 2120587

119899119904

60(6)

and 119899119904is synchrony speed 120596 is shown in

120596 =119889120579

119889119905 (7)

And 119899119904 is as in

119899119904= 60

119891119904

119901 (8)

Here 119891119904 represents the stator flux valueThe relation between flux and current in 119889-119902 axis frame is

as in (9) and (12)

120595119904119889

= 119871119904119894119904119889

+ 119871119898119894119903119889 (9)

120595119904119902 = 119871 119904119894119904119902 + 119871119898119894119903119902 (10)

120595119903119889

= 1198711015840

119903119894119903119889

+ 119871119898119894119904119889

(11)

120595119903119902 = 1198711015840

119903119894119903119902 + 119871119898119894119904119902 (12)

The state-space model according to (0 119889 119902) stator androtor axis frames of the system can be seen in (13) and (17)[34]

119889119894119904119889

119889119905=

1

120590119871119904

[minus119877119864119894119904119889

+ 120590119871119904120596119904119894119904119902

+1198711198981198771015840

119903

11987110158402119903

120595119903119889

+ 119901120596119871119898

1198711015840119903

120595119903119902

+ 119881119904119889]

(13)

119889119894119904119902

119889119905=

1

120590119871 119904

[minus119877119864119894119904119902

+ 120590119871119904120596119904119894119904119889

minus 119901120596119871119898

1198711015840119903

120595119903119889 +1198711198981198771015840

119903

11987110158402119903

120595119903119902 + 119881119904119902]

(14)

119889120595119903119889

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119889 minus1198771015840

119903

1198711015840119903

120595119903119889 + 120596119903120595119903119902 (15)

119889120595119903119902

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119902

minus 120596119903120595119903119889

minus1198771015840

119903

1198711015840119903

120595119903119902

(16)

119872119890= 119901

119871119898

1198711015840119903

(119894119904119902120595119903119889

minus 119894119904119902120595119903119902) = 119869

119889120596

119889119905+ 119861120596 (17)

Consider the following symbols

119871119904 stator winding inductance (H)

119871119903 rotor winding inductance (H)

119872119898 maximummutual inductance between rotor and

stator (H)119877119904 stator phase resistance (Ω)

119877ℎ circle peace resistance between two strips (Ω)

119877c strip resistance (Ω)119872119904119904 converse inductance between stator phase wind-

ings (H)119872119903119903 mutual inductance between rotor strips (H)

120583119900 412058710

minus7119892 air gap (m)119860 air gap segment (m2)119901 number of pole pairs119908119904 stator angular frequency

119908119903 rotor angular frequency

119908 synchronous speed119891119904 stator frequency

120595119904 stator flux vector

120595119903 rotor flux vector

120579 machine axis rotation angle119869 viscosity moment119861 viscosity friction coefficient

TheWES emulator is established by coupling two squirrelcage asynchronous machines together as seen in Figure 4The power of the machine to be used as the prime moverfor representing the wind turbine is selected as 5 kW andthe power of the other machine used as the generator isselected as 3 kW so that the generator can also be operated atoverpower conditions to expand the analysis of the operatingcases

Themachine on the left is a primemover representing thewind turbine and is controlled with a 119881119891 speed controllerDepending on the wind speed the output voltage of thegenerator the secondmachine from the left changes between

6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

4 International Journal of Photoenergy

InverterChopperMPPT and battery charge

Photovoltaic panels

Batteryunit

S1

+

I V

Loads

I V

220V50Hz

48V24V

48V DC bus

19sim72V input48V output

42sim60V input

minus

Figure 2 The PV power generation part of the system

is then obtained by connecting the PV modules in series andin parallel yielding the PV arrays [24]

The power flow path from PV power generation unit tothe load is shown in Figure 2 Four PV modules connected 2in series and 2 series branches in parallel are used here to getan array with 42 volts and 5 amperes maximum under goodweather conditions

PV array and battery groups are connected to each otherwith a device including a battery charging regulator andmaximum power point tracker When the sunlight is notsufficient the batteries step in and supply the necessarypower to the loads The MPPT is used to transfer themaximum generated power from the PV array and chargethe batteries if any power is left after feeding the load Thisunit is MOTECH PV4830 MPPT charge controller Thebatteries are also charged from the DC power bus whenthe sunlight is insufficient A battery charge regulator with12243648V and 30A is used as a charging interface deviceTotal peak power generated by the PV array under goodweather conditions is about 320Wp Since the value of thegenerated voltage from the PV array changes depending uponsunlight a DC chopper (19sim72V DC input voltage) is usedto keep the DC voltage from the PV panels at 48V Thischopper is boost converter The diode that is used after thechopper is located in order to protect the chopper It is a kindof electronic fuse It is a diode that prevents reverse currentflow with a high current This is the magnitude of the DCbus voltage which is inverted to 220V 50HzAC voltage by aboost-up inverter In this scheme the current and voltage datafrom the loads aremeasured and transferred to the computerbesides the input voltage and output current of the chopperto be used in decision making process

22 Wind Turbine Emulator and WES The wind turbinesconvert the mechanical energy that is produced by the windto electrical energy To use this electrical energy a voltageand a frequency regulation has been needed The model ofthe wind turbine is developed by the basis of the steady statepower characteristics of the turbine

Calculations of induction machines are completed in 119889-119902axis frame Figure 3 shows the depictions of axis frames

120573

Vcq

Vb

Va

ws

120593r

p120579

120572

d

120595r

120595rd

120595rq

120595r120573

120595r120572

Figure 3 Phase depiction of components of 119889-119902 axis frame in rotorflux vector

Here we can obtain

[

119881119904119889

119881119904119902

] = [

Cos 120579119904

minus Sin 120579119904

Sin 120579119904

Cos 120579119904

][

119881119904120572

119881119904120573

] (2)

After the necessary conversions are completed 119889-119902 axisframe equivalent of the machine is obtained as shown in

[[[[[

[

119881119904119889

119881119904119902

0

0

]]]]]

]

=

[[[[[

[

119877119904

0 0 0

0 119877119904 0 0

0 0 1198771015840

1199030

0 0 0 1198771015840

119903

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

+

[[[[[

[

119871 119904 0 119871119898 0

0 119871119904

0 119871119898

119871119898 0 1198711015840

1199030

0 119871119898

0 1198711015840

119903

]]]]]

]

119889

119889119905

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

International Journal of Photoenergy 5

+

[[[[[

[

0 minus120596119904119871119904

120596119904119871119898

0

120596119904119871 119904 0 0 120596119904119871119898

120596119903119871119898

0 0 minus120596119903119871119903

0 120596119903119871119898 120596119903119871119903 0

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

(3)

119872119890= 119901119871119898(119894119904119902119894119903119889 minus 119894

119904119889119894119903119902) = 119869

1198892120579

1198891199052+ 119861

119889120579

119889119905 (4)

Angular frequency 120596119904is as shown below

120596119904= 120596119903+ 119901120596 (5)

Here 120596119904 represents the angular frequency of stator fluxesand 120596119903 represents rotor fluxes angular frequency and 120596

angular speed of machine shaft also represented by

120596119904=

119889120579

119889119905= 2120587

119899119904

60(6)

and 119899119904is synchrony speed 120596 is shown in

120596 =119889120579

119889119905 (7)

And 119899119904 is as in

119899119904= 60

119891119904

119901 (8)

Here 119891119904 represents the stator flux valueThe relation between flux and current in 119889-119902 axis frame is

as in (9) and (12)

120595119904119889

= 119871119904119894119904119889

+ 119871119898119894119903119889 (9)

120595119904119902 = 119871 119904119894119904119902 + 119871119898119894119903119902 (10)

120595119903119889

= 1198711015840

119903119894119903119889

+ 119871119898119894119904119889

(11)

120595119903119902 = 1198711015840

119903119894119903119902 + 119871119898119894119904119902 (12)

The state-space model according to (0 119889 119902) stator androtor axis frames of the system can be seen in (13) and (17)[34]

119889119894119904119889

119889119905=

1

120590119871119904

[minus119877119864119894119904119889

+ 120590119871119904120596119904119894119904119902

+1198711198981198771015840

119903

11987110158402119903

120595119903119889

+ 119901120596119871119898

1198711015840119903

120595119903119902

+ 119881119904119889]

(13)

119889119894119904119902

119889119905=

1

120590119871 119904

[minus119877119864119894119904119902

+ 120590119871119904120596119904119894119904119889

minus 119901120596119871119898

1198711015840119903

120595119903119889 +1198711198981198771015840

119903

11987110158402119903

120595119903119902 + 119881119904119902]

(14)

119889120595119903119889

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119889 minus1198771015840

119903

1198711015840119903

120595119903119889 + 120596119903120595119903119902 (15)

119889120595119903119902

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119902

minus 120596119903120595119903119889

minus1198771015840

119903

1198711015840119903

120595119903119902

(16)

119872119890= 119901

119871119898

1198711015840119903

(119894119904119902120595119903119889

minus 119894119904119902120595119903119902) = 119869

119889120596

119889119905+ 119861120596 (17)

Consider the following symbols

119871119904 stator winding inductance (H)

119871119903 rotor winding inductance (H)

119872119898 maximummutual inductance between rotor and

stator (H)119877119904 stator phase resistance (Ω)

119877ℎ circle peace resistance between two strips (Ω)

119877c strip resistance (Ω)119872119904119904 converse inductance between stator phase wind-

ings (H)119872119903119903 mutual inductance between rotor strips (H)

120583119900 412058710

minus7119892 air gap (m)119860 air gap segment (m2)119901 number of pole pairs119908119904 stator angular frequency

119908119903 rotor angular frequency

119908 synchronous speed119891119904 stator frequency

120595119904 stator flux vector

120595119903 rotor flux vector

120579 machine axis rotation angle119869 viscosity moment119861 viscosity friction coefficient

TheWES emulator is established by coupling two squirrelcage asynchronous machines together as seen in Figure 4The power of the machine to be used as the prime moverfor representing the wind turbine is selected as 5 kW andthe power of the other machine used as the generator isselected as 3 kW so that the generator can also be operated atoverpower conditions to expand the analysis of the operatingcases

Themachine on the left is a primemover representing thewind turbine and is controlled with a 119881119891 speed controllerDepending on the wind speed the output voltage of thegenerator the secondmachine from the left changes between

6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 5

+

[[[[[

[

0 minus120596119904119871119904

120596119904119871119898

0

120596119904119871 119904 0 0 120596119904119871119898

120596119903119871119898

0 0 minus120596119903119871119903

0 120596119903119871119898 120596119903119871119903 0

]]]]]

]

[[[[[

[

119894119904119889

119894119904119902

119894119903119889

119894119903119902

]]]]]

]

(3)

119872119890= 119901119871119898(119894119904119902119894119903119889 minus 119894

119904119889119894119903119902) = 119869

1198892120579

1198891199052+ 119861

119889120579

119889119905 (4)

Angular frequency 120596119904is as shown below

120596119904= 120596119903+ 119901120596 (5)

Here 120596119904 represents the angular frequency of stator fluxesand 120596119903 represents rotor fluxes angular frequency and 120596

angular speed of machine shaft also represented by

120596119904=

119889120579

119889119905= 2120587

119899119904

60(6)

and 119899119904is synchrony speed 120596 is shown in

120596 =119889120579

119889119905 (7)

And 119899119904 is as in

119899119904= 60

119891119904

119901 (8)

Here 119891119904 represents the stator flux valueThe relation between flux and current in 119889-119902 axis frame is

as in (9) and (12)

120595119904119889

= 119871119904119894119904119889

+ 119871119898119894119903119889 (9)

120595119904119902 = 119871 119904119894119904119902 + 119871119898119894119903119902 (10)

120595119903119889

= 1198711015840

119903119894119903119889

+ 119871119898119894119904119889

(11)

120595119903119902 = 1198711015840

119903119894119903119902 + 119871119898119894119904119902 (12)

The state-space model according to (0 119889 119902) stator androtor axis frames of the system can be seen in (13) and (17)[34]

119889119894119904119889

119889119905=

1

120590119871119904

[minus119877119864119894119904119889

+ 120590119871119904120596119904119894119904119902

+1198711198981198771015840

119903

11987110158402119903

120595119903119889

+ 119901120596119871119898

1198711015840119903

120595119903119902

+ 119881119904119889]

(13)

119889119894119904119902

119889119905=

1

120590119871 119904

[minus119877119864119894119904119902

+ 120590119871119904120596119904119894119904119889

minus 119901120596119871119898

1198711015840119903

120595119903119889 +1198711198981198771015840

119903

11987110158402119903

120595119903119902 + 119881119904119902]

(14)

119889120595119903119889

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119889 minus1198771015840

119903

1198711015840119903

120595119903119889 + 120596119903120595119903119902 (15)

119889120595119903119902

119889119905=

1198771015840

119903119871119898

1198711015840119903

119894119904119902

minus 120596119903120595119903119889

minus1198771015840

119903

1198711015840119903

120595119903119902

(16)

119872119890= 119901

119871119898

1198711015840119903

(119894119904119902120595119903119889

minus 119894119904119902120595119903119902) = 119869

119889120596

119889119905+ 119861120596 (17)

Consider the following symbols

119871119904 stator winding inductance (H)

119871119903 rotor winding inductance (H)

119872119898 maximummutual inductance between rotor and

stator (H)119877119904 stator phase resistance (Ω)

119877ℎ circle peace resistance between two strips (Ω)

119877c strip resistance (Ω)119872119904119904 converse inductance between stator phase wind-

ings (H)119872119903119903 mutual inductance between rotor strips (H)

120583119900 412058710

minus7119892 air gap (m)119860 air gap segment (m2)119901 number of pole pairs119908119904 stator angular frequency

119908119903 rotor angular frequency

119908 synchronous speed119891119904 stator frequency

120595119904 stator flux vector

120595119903 rotor flux vector

120579 machine axis rotation angle119869 viscosity moment119861 viscosity friction coefficient

TheWES emulator is established by coupling two squirrelcage asynchronous machines together as seen in Figure 4The power of the machine to be used as the prime moverfor representing the wind turbine is selected as 5 kW andthe power of the other machine used as the generator isselected as 3 kW so that the generator can also be operated atoverpower conditions to expand the analysis of the operatingcases

Themachine on the left is a primemover representing thewind turbine and is controlled with a 119881119891 speed controllerDepending on the wind speed the output voltage of thegenerator the secondmachine from the left changes between

6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

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6 International Journal of Photoenergy

R S T Inductionmachine

Inductiongenerator

Speedcontrol

Transformer

Wind turbine emulator

Chopper

S2

Inverter

Loads

Rectifier

MPPT

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

48V DC bus

220V50Hz

48V

42sim60V input

Vsim51Voutput voltage

I V

I V

1 1055

405

Figure 4 Feeding the loads with power generated fromWES model

RectifierGridTransformer

Chopper

S3

DC bus

Inverter

Loads

I V

I V

outputoutput voltage 220V50Hz

220V50Hz

48V

48V

48V220V48V

42sim60V input

432V 19sim72V input

Figure 5 Grid connection structure in the system

320V and 400V Three phase voltage magnitudes obtainedfrom the generator are reduced to a lower level using astep down transformer with a ratio of 380V36V Thereforetransformer output voltages change between 30V and 38VThe output voltage magnitude of 3-phase full bridge dioderectifier is calculated as in

119880119900= 1654 sdot 119880

119877MAX (18)

where 119880119900is rectifier output voltage (V) and 119880

119877MAXis the

maximum value of single phase voltage (V) Full bridgerectifier output voltage changes from 405V to 51 V A DCchopper is used to convert this variable voltage to a 48Vconstant DC to be connected to common DC bus which hasa 48V constant DC voltage

The reactive power to generate energy at this point issupplied with a condenser group Later on this three-phasevoltage which is rectified is sent through a chopper and itis brought to 48V value and connected to common DCbus Then an inverter is used to convert this 48V voltage to230V50 Hz alternating voltage and the loads are fed Herethe current and voltage information on the loads and copperinput voltage and output current ismeasured and these valuesare transferred to the computer

23 Grid Connection The proposed renewable energyscheme consists of two-type operating conditions The firstcase described in previous sections is the one supplyingpower to individual loads where the utility grid is notavailable or not connected The second case deals with utilityconnected renewable energy scheme as shown in Figure 5Hence power generated from each power generation systemis collected in 48V direct current bus

As shown in Figure 4 the utility voltage is converted to48VDC constant value to be connected to 48V commonDCbus as it is done for wind and PV systems

Data collected from various parts of the system is trans-ferred to the computer and used for control and decisionmaking processes The interfacing between the real systemand the computer is established by NI USB 6259 dataacquisition card

24 Power Management and Sustainability The electricalpower generated by renewable sources such as wind and solarpower is affected by environmental conditions resulting inproblems in load part When there is no sun or the weatheris cloudy the power amount to be generated by solar energy

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 7

Wind systempower

Base load power Open secondary power system

Consumed power

Yes

No

Wind systempower

Loadsgt0

(330W)

minus

minus

+

Figure 6 Basic block diagram of power management system

changes Accordingly wind does not blow at the same speedall the time it is discontinuous Henceforth energy amountto be generated from these sources are variable

In particular in low powered applications for instancewhile supplying energy to a house from these sources it is aproblematic situation to have a power cutoff while watchinga TV Power sources must be efficiently operated in order toavoid such situations

Proposed PMA and decision making process are devel-oped to prevent problems like voltage sags and discontinuitiesthat occur due to either weather changes or sudden loadchanges The intelligent decision making algorithm managesthe energy storage and usage switching patterns so thatenergy sustainability is guaranteed

General block diagramof the powermanagement schemeis given in Figure 6The total generated power from the windis used as the primary supply power which is used to supplybase load power As long as the wind power is sufficient thesecondary power system is kept off and additional generatedpower is stored When the wind power is less than therequired load power then the secondary power system whichis solar andor utility grid is activated The service order ofthe secondary power system goes as solar PV storage andutility grid Since the utility grid requires additional paymentit is put at the end of the list and the priority is given to thewind and PV arrays [35] The load power is calculated as in

119875Load = 119875WindSystem + 300W (19)

25 Power Management System Design In order to solvesustainability and power quality problems the power transferfrom the renewable sources to load must be managed ina proper way Therefore a PMA system has been designedto prevent power discontinuity and overvoltage and under-voltage operations so that the loads operate properly Thepower management system is automated in an efficient wayby switching on or off the sources and backup units Forexample if the wind power is sufficient enough to feed theload then there is no need for the auxiliary sources of PVbackup batteries and the utility If the wind power decreasesthe gap is filled by PV first then batteries and then theutilityThe overgenerated power is stored and used only whenneeded

The overall energy generation system established experi-mentally can be seen in Figure 7 In this system the electricalpower is generated by wind generator and PV solar panelsThe utility is reserved as an auxiliary source to be used whenneeded The power from the PV system is used to supply

power to the load when the wind power is not sufficient andto charge the batteries when there is sufficient wind and sunpower Data collected from various parts of the overall systemis transferred to the computer to be analyzed

The main objective of employing a power managementalgorithm in power systems where the renewable energy isthe priority supply is to have the power ready to be used andfeed the load continuously For this reason the peak powervalue from bothWES and PV solar panelsmust be calculatedMPPTdevice used for PV solar panels handles this duty on itsown Therefore there is no need to calculate the peak powercalculation in PV solar panels MOTECH PV4830 MPPTcharge controller calculates MPP by itself However the peakpower value to be obtained from WES must be defined Thepriority supply is WES in this system At the same time sincethe changing environmental conditions affect the amountof energy to be produced power management is plannedto avoid this effect by leaving a base power in the systemThe base power is the power that must be supplied all thetime for the loads with nonstop operating behaviors Thebase power in the proposed system is defined as 300Wwhich can be easily changed inside the software if desiredThe system is designed to feed maximum 1 kW which ismore than three times the base power If the environmentalconditions are sufficient which means there is enough sunandwindwind energy generation systemandPV solar panelsgeneration system produce the maximum power they canand the installed power can rise over 2 kW value The mainoperational principle of the system is summarized as follows

(1) Initially the system is started with both solar and windenergy in service

(2) After the transients are over and measurements aredone WES or PV solar panels system will be kept workingaccording to load condition If the environmental conditionsare not suitable for PV solar panels or WES to operateindividually both will operate When the present conditiondoes not meet the energy requirements of the load grid will

(3) If WES can handle the load power requirement aloneit will operate PV solar panels will be used to charge thebatteries only

(4) If WES does not generate sufficient power PV solarpanels will engage and both will operate together If both areinsufficient then the grid will engage

(5) If there is no wind PV solar panels will feed the loads(6) When there is no sun and the batteries are empty

or the battery cannot feed the loads with its actual poweramount the grid will begin to operate These steps bring upthe importance of the following

(1) the operating time of each unit(2) turnoff time of each unit(3) the amount of load at present conditions

Since only the wind energy will operate constantly theselists of rules are processed by taking the measurements fromwind energy system into consideration

(1) 300Wpower will be supplied as the permanent powerin the system The wind turbine emulator is operated atvarious speeds in order to represent the generated wind

8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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8 International Journal of Photoenergy

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

RectifierGridTransformer

ChopperS3

I V

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

48V output19sim72V input

220V50Hz

220V50Hz

48V

48V

48V

24V

42sim60V input

Vsim51Voutput voltage

I V

I V

I V

minus

output voltage 220V48V

1 1055

432V

405

48V DC Bus

Figure 7 PV solar panels-WES-grid system

power properly The emulator has been set up so that thegenerated power will not be sufficient with the base powerunder a speed of 35Hz During this condition solar energywill automatically begin to operate

(2) 119881DCDC119866 chopper input voltage value is different inasynchronous motor driver frequencies For instance whenthe load power is 500W the generated voltage becomes 473 Vat 44Hz and 454V at 43Hz It will decrease other valuedown to 315 V at 37HzThe relationship between voltage andfrequency is used to estimate the operating frequency

(3) Considering both 119868DCDC119874 and 119881DCDC119866 values asyn-chronous motor driver frequency power dissipated at thatmoment and maximum load values at that driver frequencycan be detected This case is also used to determine switchon or switch off times of PV solar panels It should not beforgotten that 300W power will always be in the system asthe base power to be supplied

(4) Similarly when the PV solar panels are on the gener-ated voltage from the PV panels to the chopper decreases asload power increases Using the measured values from thisoperating case it is possible to find out how much powershould be generated by the panels to feed the load Chopperinput voltage value will decrease as the batteries dischargeThe batteries are assumed to be insufficient to feed the loadwhen their voltage drops down below 21V If the sunlightis not enough to generate the required power to the load

the batteries also will not be usable with an output voltageless than 21 V

(5) While PV solar panels are feeding the loads afterthe WES begins to operate some of the required power issupplied fromWES If WES can supply all of the load powerthen PV solar panels are switched to charge the batteries

(6) It is important to get measurements of these operatingcases continuously in order to respond immediately insudden load changes The base power demand is 300W Ifa sudden load change increases over 300W the system isdisconnected For example when the load power is 500Wwhen WES can give maximum 500W PV solar panels mustoperate so that in a 300W sudden load increase the loadscan be fed without the system failure This action is takento prevent system shutdown in power changes resulting fromsudden charges according to the environmental conditions atthe time

(7) The grid connection is established when neither thesun nor the wind are sufficient enough to supply 300W basepower demand

Flow diagram of power management program is given inFigure 8

26 Fuzzy Logic Reasoning (FLR) and Calculation of PeakWind Power Fuzzy logic has so many applications in indus-try Fuzzy logic reasoning (FLR) algorithms are generally

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 9

Start

PV onWES on

Measurements

Yes

No

PV and grid off

PV on

Yes

No

Grid on

Ptotal = PR + 300W

Ptotal ge PL

VG gt 20V

Figure 8 Simplified flow diagram of power management program

Input variables(crisp)

FuzzificationFuzzyrulebase

Fuzzyconclusion

DefuzzificationCrispout

Fuzzyoutputs

FDM

Figure 9 Fuzzy decision maker

used in fuzzy decision makers (FDM) which find applica-tions in systems that require a conclusion from uncertaininput data Since the wind conditions are not certain andare not easily predictable the power generated by the windenergy system becomes uncertain including the maximumgenerated power as well Therefore a fuzzy reasoning algo-rithm is developed to determine the maximum power gen-erated by WES The fuzzy reasoning applied to determinethe maximum power of the WES is represented in Figure 9Fuzzy decisionmakerrsquos MATLABSimulinkmodel is given inFigure 10

A FDM usually gets fuzzy inputs and evaluates them inthe rule base system which is set up earlier representing theinput-output relations of the uncertain system in terms offuzzy membership functions and fuzzy rules (FR) The FRis the evaluation of the rules to yield fuzzy conclusions fromfuzzy inputs-fuzzy rules interactions if the input variables are

crisp Certain input values are rated here Thus these valuescan be included in a value range that can be used by thecontroller and then it can be expressed verbally In addition itbecomes amember of a group that has clear boundariesThenthey are fuzzified to fuzzy values Similarly if the output isrequired as crisp value then the concluded fuzzy outputs areconverted to crisp values by the process called defuzzification

In this study the input values to the FDM are the currentand voltage measured from theWESThe generated rules areused to relate the input voltage and current with the powerof the WES depending upon the wind speed conditions FRalgorithm in the FDM is used to obtain maximum powergeneration fromWES for uncertain and unpredictable speedconditions The designed FR based FDM is modeled inMatlabSimulink environment as shown in Figure 10 Themaximum wind power value is determined by FDM usingchopperrsquos input voltage 119881DCDC119868 and the output current is119868DCDC119874 The wind turbine a 3-phase transformer a 3-phasebridge rectifier and a DCDC converter are all assumed as awhole system All of the calculations are based on the valuesof 119881DCDC119868 and 119868DCDC119874 Since the chopper output voltagesare kept constant at 48V DC the main variable at the outputterminals of the choppers or at commonDCbus is the currentat the output terminals of the choppersThe output current ofthe chopper used to control the voltage ofWES is the variablethat reflects the changes on WES power Therefore the activepower generated by the WES is obtained from

119875DCDC = 48V times 119868DCDC119874 (20)

where 48V is the chopper output voltageAll of the MPPT calculations are based on the values of

119881DCDC119868 and 119868DCDC119874 As the wind speed changes producedpower values change Therefore current and voltage valuesthat belong to system have changed too These changes arefollowedwith the chopper and its values119881DCDC119868 and 119868DCDC119874 By following these two values the peak power value of thesystemrsquos present speed value has been determined in this wayThose loading experiments were made for all wind speedvalues first and then power for each speed was determinedand transferred to the controller by blurring them Controlleruses abovementioned experience and control the systemproperly It is a kind of expert system behavior

The online data collected and transferred to computer isused to determine the amount of load power demand thatsupplied from the PVwind sources In the meantime thedata representing the WES quantities are used by FDM todetermine themaximumpower generated by theWES for theinstant themeasurementsmadeThemaximumpower valuesof both WES and PV panels are used in power managementpart of the study As seen in Figure 11 the crisp currentand voltage values are converted to fuzzy values Triangletype membership functions are used to represent 7 fuzzysubsets In the system the current values and voltage valuesare varying from 35 A to 235 A (from 119868

1to 1198686) and from

27V to 55V (from 1198811to 1198816) respectively The lower limit of

the voltage corresponds to the lower limit of the wind speedBelow the lower limit of the voltage the required energyconversion is not satisfied Therefore the voltages below 27V

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

10 International Journal of Photoenergy

FuzzificationRules

akimCurrent

NBiNBi

NOiNOi

NKiNKi

SiSi

PKiPKi

POiPOi

PBiPBi

GerilimVoltage

NBvNBv

NOvNOv

NKvNKv

SvSv

PKvPKv

POvPOv

PBvPBv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

NBiNBvNBiNOvNBiNKv

NBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKv

NOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKv

NKiSvNKiPKvNKiPOvNKiPBv

SiNBvSiNOvSiNKv

SiSvSiPKvSiPOvSiPBv

PKiNBvPKiNOvPKiNKv

PKiSvPKiPKvPKiPOvPKiPBv

POiNBvPOiNOvPOiNKv

POiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKv

PBiSvPBiPKvPBiPOvPBiPBv

NBiNBvNBiNOvNBiNKvNBiSv

NBiPOvNBiPKv

NBiPBvNOiNBvNOiNOvNOiNKvNOiSvNOiPKvNOiPOvNOiPBvNKiNBvNKiNOvNKiNKvNKiSvNKiPKvNKiPOvNKiPBvSiNBvSiNOvSiNKvSiSvSiPKvSiPOvSiPBvPKiNBvPKiNOvPKiNKvPKiSvPKiPKvPKiPOvPKiPBvPOiNBvPOiNOvPOiNKvPOiSvPOiPKvPOiPOvPOiPBvPBiNBvPBiNOvPBiNKvPBiSvPBiPKvPBiPOvPBiPBvNBduNOduNKduSduPKduPOduPBdu

NBdu

NOdu

NKdu

Sdu

PKdu

POdu

PBdu

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+++++++

+++++++

++

++++++

+++++++++++++++++++++++++++

divide

Out 1Out 2Out 3Out 4Out 5Out 6Out 7Out 8Out 9Out 10Out 11Out 12Out 13Out 14Out 15Out 16Out 17

17

1

Out 18

18

Out 19

19

Out 20

20

Out 21

21

Out 22

22

Out 23

23

Out 24Out 25Out 26Out 27Out 28Out 29Out 30Out 31Out 32Out 33Out 34Out 35Out 36Out 37Out 38Out 39Out 40Out 41Out 42Out 43Out 44Out 45Out 46Out 47Out 48Out 49

times

Figure 10 Fuzzy logic unit

are treated as zero The output of the FDM is the outputpower space (from 1198821 to 1198826) that calculates the maximumpower value provided by the WES The power generated bythe WES varies as a function of the wind speed Thereforethe maximum power was obtained from the WES changesdepending on the wind speed levels and must be determinedfor different speed levels as thewind speed changesThereforethe maximum power surface of the WES is portioned intoseven fuzzy subsurfaces as shown in Figure 12 [24]

Experimental test system is seen in Figures 13 and 14

In WES MPTT is implemented as software There is notany electronic intervention to the generator In addition tothis there is not any added hardware For this reason it isdifferent from other MPTT systems

3 Test and Results

In Figure 15 the system consisting of WES and PV solarpanels andwithout energymanagement software can be seen

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 11

35 2016 2350 683 1016 135 1683

1

Universe of current (A)

120583(c

urre

nt)

I1 I2 I3 I4 I5 I6 I7

(a) Fuzzy subsets of current

27 445 480 305 34 375 41

1

Universe of voltage (V)

120583(v

olta

ge)

V1 V2 V3 V4 V5 V6 V7

(b) Fuzzy subsets of voltages

Figure 11 FLR inputs

Universe of active power (W)100 850 10000 250 400 550 700

1

120583(p

ower

)

W1 W2 W3 W4 W5 W6 W7

Figure 12 Fuzzy subset of power output spaces

Figure 13 Appearance of experimental system on one side

The system fully operates in free mode There is neithersupervision nor a control mechanism

As the first operating case the system is analyzed whenboth wind and PV solar panels are on without applying anypower management For this case chopper input voltage 119881

119877

in WES chopper output current 119868119877 PV solar panel voltage

119881119866 to the chopper input chopper output current 119868

119866 load

voltage119881119884 and load current 119868

119884 can be seen in Figures 16ndash18

It can be seen that when a load power of 800W ison WES and PV system together cannot feed the loadsfrom 40 s to 70 s and from 86 s to 89 s since they couldnot operate properly Besides the load voltage decreases

Figure 14 Appearance of experimental system from above

down to 0 sometimes instead of remaining at the requiredvalue of 220V Although there is enough power generatedin the system power discontinuities occur Actually thepower generated PV solar panels can be used to eliminatethe power discontinuities due to changes in wind speed byapplying proper power management algorithms In this casea decisionmaking system is needed to control the system andmake decisions in order to avoid lack of power discontinuityon loads

The proposed powermanagement algorithm is realized inMATLABSimulink environment using dynamic operationalblocks library as shown in Figure 19

The Simulink diagram in Figure 18 consists of the follow-ing subsystems

R I ok wind system currentR V ok wind system voltageR P ok wind system powerG V ok PV system voltageG I ok PV system currentY P ok loads powerPwind Max calculated maximum wind powerGrid Switch grid system switch (onoff)PV Switch PV system switch (onoff)

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

12 International Journal of Photoenergy

Transformer

Rectifier

320sim400V input30sim38V output

320sim400V output voltage

19sim72V input48V output

19sim72V input48V output

220V50Hz

48V

48V24V

42sim60V input

output voltage

I V

I V

I V

InverterChopperMPPT and

battery charge

Photovoltaic panels

Battery unit

S1

+ minus

Loads

R S T Inductionmachine

InductiongeneratorSpeed

control

Wind turbine emulator

Chopper

S2

1 1055 Vsim51V405

48V DC Bus

Figure 15 Uncontrolled wind-solar energy generation system

0 10 20 30 40 50 60 70 80 90 1000204060

Wind energy system output voltage

0

0

510

Wind energy system output current

500

Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Wind energy power

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 16 Voltage current and power variations of WES for case 1

Figure 20 shows current voltage and power changes ofWES for operating case 2 in which the load power is changedarbitrarily in different time periods resulting in changes inthe quantities of WES It can be observed that WES is alwaysactive yet because of wind speed and powers extracted thepower amount transferred to the system changes

Current voltage and power changes for PV systemduring case 2 can be seen in Figure 21 PVpower is supplied tothe load between the durations 119905 = 20ndash75 s and 119905 = 88ndash100 s

0102030

PV system output voltage

01020

PV system output current

0500

1000PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 17 Voltage current and power change of PV solar panelsfor case 1

The changes of current voltage and power from theutility grid are given in Figure 22 The grid transfers energyto the loads during the time interval of 119905 = 45ndash68 s

Current voltage and power changes on the load terminalscan be seen in Figure 23The load amount changes constantlyThere is a power consumption varying between 0W and900W 100W lamps are used as load Voltage on the load is220V Varying amounts of current are taken from the energygeneration system according to the load condition

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 13

0100200

Load voltage

05

10 Load current

010002000

Load power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

Figure 18 Voltage current and power change of loads for case 1

In Figure 24 variation of the power generated by WES isgiven A fuzzy logic reasoning (FLR) algorithm is employedto manage this power

In Figures 25 and 26 it can be observed that PV andgrid system operates and switch on and off During the timeinterval of 119905 = 41ndash68 s the PV system is on while during thetime interval of 119905 = 45ndash58 s the utility grid system feeds theloads Both systems operate for short time periods from timeto time depending upon the sudden changes

In Figure 27 we can see the details of voltage change onthe loads Wave shape is very close to sine Distortions areobserved since data is generally taken from voltage detector

Between Figures 28 and 37 an asynchronous motor isadded to lamps which are used as load and the system isrestarted and then the results are drawn Thus by addingresistive load and also inductive load to the system thebehavior of the system under different load conditions isexamined in detail

The current voltage and power value changes fromWEScan be seen in Figure 28 The magnitude of the generatedvoltage is very low in time interval from 50 s to 70 s due tohigh reduction in wind speed

Time variations of voltage current and power of PV sys-tem are given in Figure 29 PV solar panels operate especiallyin period from 45 s to 70 s PV solar panels are active duringthis period because WES is not sufficient enough to generatethe required load power

Figure 30 shows the variations of voltage current andpower of the utility grid During the period from 45 s to 75 sthe utility grid supplies power to the loads since both windandPV systems are off or they donot generate required powerdue to low speed andor low sunlight

Current voltage and power changes on the load canbe seen in Figure 31 Asynchronous motor load is alwayskept operating During the time interval of 119905 = 3ndash18 s200W lamps are added to the system as loads in additionto the asynchronous motor Later in period 119905 = 27ndash85 s thelamps are turned off leaving only the asynchronous motorsas the load Meanwhile wind speed is changed constantly

Load voltage is kept at 220V while the load current variesaccording to load condition

The maximum power drawn from the WES is given inFigure 32 The generated power by WES is high around 900ndash950W when the wind speed is high and it is low around200W when the wind speed is lower during the period from50 s to 70 s

The on and off switching instances of PV solar panels andutility grid system are shown Figures 33 and 34 respectivelyIn period of 119905 = 40ndash70 s PV panels are on and in period of119905 = 43ndash70 s the grid system is onThey are turned on in orderto supportWES so that the loads will not be lacking of energyin that time period

Wave shape details of voltage on the load are given inFigure 35 Although elements like asynchronous motor leadto harmonics a very clear sine wave with a little amount ofdistortion is obtained

The variations of power from wind PV and load bussesare given in Figure 36 for case 1 where there is neither controlnor power management algorithm Since there is no powermanagement the load tries to get the power from PV andbackup system which are not sufficient enough to feed theload Therefore load does not get the required power tooperate

Variations of the power from WES PV and utility gridalong with load power and available maximum value ofthe wind power are shown in Figure 37 Proposed powermanagement algorithm (PMA) has been applied for this caseTherefore there is no problem on supplied load power Asload or environmental conditions change the load power issupplied from the sources in the order of priority use as windPV and utility grid If the power from the wind is sufficientfor the load the generated PV power is stored in backupbatteries If the wind power is not sufficient then PV power isconnected to complete the required power If both wind andPV do not generate the required load power then the utilitygrid is connected

4 Conclusions

An intelligent energy management system (IEMS) for main-taining the energy sustainability in renewable energy sys-tems is presented in this study A renewable energy systemconsisting of wind and PV panels is established and usedto test the proposed IEMS Since the wind and PV sourcesare not reliable in terms of sustainability and power qualitya management system is required for sustainability on theload side The proposed PMA is used to collect power fromrenewable sources and utility grid at a common DC busand feed the loads preventing any power discontinuity Byemploying PMA a base power is always supplied to DCbus to be used by the loads that are operating permanentlyBesides PMA handles the effects of the changes in windspeed solar irradiation and amount of the load by operat-ing wind PV and utility grid accordingly using intelligentdecision making abilities The proposed intelligent PMA isalso used to determine and track the generated and availablemaximum wind power from WES so that the efficiency of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

14 International Journal of Photoenergy

Ts = 500120583sDiscreteTs = Ts sPowergui

G_I_okG_V_ok

Y_P_ok

Y_P_ok

times

times

times

+

++

+

+

minus+

+

minus

minus

0

5

5

2

5

lt

le

ge

Pwind_Max

Pwind_Max

Pwind_Max

PV_Switch

PV_Switch

300

300

Nor

Reserve power

Current

VoltageOut

Switch

Mean(discrete)

Grid_Switch

R_I_ok

R_V_ok

R_P_ok

Figure 19 Simulink model of the proposed power management system

0

50Wind energy system output voltage

05

1015

Wind energy system output current

0500

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 20 Current voltage and power change in WES for case 2

0102030

PV system output voltage

05

1015

PV system output current

0500

PV system power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 21 Current voltage and power change of PV system in case2

050

Grid system voltage

05

10Grid system current

0

500Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 22 Variations of current voltage and power from the utilitygrid in case 2

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 23 Current voltage and power change on the load in case2

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 15

0 10 20 30 40 50 60 70 80 90 1000

200

400

600

800

1000

1200

Time (s)

Calculated maximum wind power

Pw

indt

urbi

ne(W

)

Figure 24 Peak power changes obtained from WES using FLRalgorithm

minus1

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 25 Switching instants of PV systemwhenwind energy is notsufficient

0

1

2

3

4

5

6

Grid

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 26 Switching instants of utility grid when wind and solarenergy are not sufficient

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus400minus300minus200minus100

0100200300400

Time (s)

Vlo

ads

(V)

Figure 27 Wave shape of the voltage on loads

0

50Wind energy system output voltage

0

0

1020

Wind energy system output current

5001000

Wind energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Vw

ind

(V)

I win

d(A

)P

win

d(W

)

Figure 28 Current voltage and power changes of wind energysystem

0102030

PV energy system output voltage

0

5PV energy system output current

0100200300

PV energy power

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

VPV

(V)

I PV

(A)

PPV

(W)

Figure 29 Current voltage and power change of PV energy system

050

Grid system voltage

0

5Grid system current

0100200

Grid system power

Vgr

id(V

)I g

rid(A

)P

grid

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 30 Current voltage and power change of grid system

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

16 International Journal of Photoenergy

0100200

Loads voltage

0

5Loads current

0500

1000Loads power

Vlo

ads

(V)

I loa

ds(A

)P

load

s(W

)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 31 Current voltage and power change on the loads

0100200300400500600700800900

1000Calculated maximum wind power

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)

Figure 32 The maximum power change tracking of the WES byFLR

0

1

2

3

4

5

6

PV p

ower

switc

hing

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 33 On and off switching pulses of PV panels

the installed units is increased Using the generated andrequired power information from the windPV and loadsides the fuzzy reasoning based PMA generates the requiredoperating sequences to manage the overall system powerwith the minimum requirement from the utility The IPMSis also designed to operate the renewable energy systems

Grid

switc

hing

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100Time (s)

minus1

Figure 34 On and off switching pulses of utility grid

50 5001 5002 5003 5004 5005 5006 5007 5008 5009 501minus500minus400minus300minus200minus100

0100200300400500

Time (s)

Vlo

ads

(V)

Figure 35 Voltage wave shape on the load

0

500Wind energy power

0500

1000PV energy power

010002000

Loads power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Figure 36 Power changes in the system without power manage-ment

as a part of power utility Therefore the IEMS can also beconsidered as a smart grid operator in the proposed RESapplication Proposed IPMS can be extended to be used indistributed power systems for providing decisions on powermanagement during critical peak power instances and fastpower demand changes

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 17

0500

Wind energy power

0500

PV energy power

0500

1000 Loads power

0500

1000Calculated maximum wind power

0500

Grid power

PPV

(W)

Pw

ind

(W)

Plo

ads

(W)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

0 10 20 30 40 50 60 70 80 90 100Time (s)

Pw

m(W

)P

grid

(W)

Figure 37 Power changes under only resistive loads condition indeveloped power management program in solar-wind-grid system

Symbols

119868119862 Cell output current

119868PH Photocurrent function of irradiation leveland junction of temperature

119868119878 Reverse saturation of current of diode

119881119862 Cell output voltage

119877119878 Series resistance of cell119890 Electron charge119896 Boltzmann constant119879pil Reference cell operating temperature119860 Curve fitting factor119881DCDC119866 Chopper input voltage119868DCDC119874 Chopper output current119881119877 WES chopper input voltage

119868119877 WES chopper output current

119881119866 PV solar panels system chopper input

voltage119868119866 PV solar panels system chopper output

current119881119884 Load voltage

119868119884 Load current

R I ok Wind system currentR V ok Wind system voltageR P ok Wind system powerG V ok PV system voltageG I ok PV system currentY P ok Loads powerPwind Max Calculated maximum wind powerGrid Switch Grid system switch (onoff)PV Switch PV system switch (onoff)119875Load Load power

119875WindSystem WEC system power119871 119904 Stator winding inductance (H)119871119903 Rotor winding inductance (H)119872119898 Maximum mutual inductance between rotor

and stator (H)119877119904 Stator phase resistance (Ω)

119877ℎ Circle peace resistance between two strips

(Ω)119877c Strip resistance (Ω)119872119904119904 Converse inductance between stator phase

windings (H)119872119903119903 Mutual inductance between rotor strips (H)

120583119900 412058710

minus7

119892 Air gap (m)119860 Air gap segment (m2)119901 Number of pole pairs119908119904 Stator angular frequency

119908119903 Rotor angular frequency119908 Synchronous speed119891119904 Stator frequency120595119904 Stator flux vector120595119903 Rotor flux vector120579 Machine axis rotation angle119869 Viscosity moment119861 Viscosity friction coefficient

Nomenclature

IEMS Intelligent energy management systemPMS Power management systemPMA Power management algorithmRES Renewable energy systemsPV PhotovoltaicPVES Photovoltaic energy systemMPPT Maximum power point trackingWES Wind energy systemDC Direct currentAC Alternative currentFLR Fuzzy logic reasoningFDM Fuzzy decision maker

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This study was supported by Karadeniz Technical UniversityScientific Research Projects Unit Project no 20081120042

References

[1] P Denholm R Margolis T Mai et al ldquoBright future solarpower as a major contributor to the US gridrdquo IEEE Power andEnergy Magazine vol 11 no 2 pp 22ndash32 2013

[2] J Von Appen M Braun T Stetz K Diwold and D GeibelldquoTime in the sun the challenge of high PV penetration in the

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

18 International Journal of Photoenergy

German electric gridrdquo IEEE Power and EnergyMagazine vol 11no 2 pp 55ndash64 2013

[3] K Ogimoto I Kaizuka Y Ueda and T Oozeki ldquoA good fitJapanrsquos solar power program and prospects for the new powersystemrdquo IEEE Power and EnergyMagazine vol 11 no 2 pp 65ndash74 2013

[4] V QuaschingUnderstanding Renewable Energy Systems Earth-scan London UK 2005

[5] T Ackermann Wind Power in Power Systems John Wiley ampSons Chichester UK 2005

[6] I Akova Yenilenebilir Enerji Kaynakları Nobel Yayin DagitimAnkara Turkey 2008

[7] C Kocatepe M Uzunoglu R Yumurtacı A Karakas andO Arikan Elektrik Tesislerinde Harmonikler Birsen YayıneviIstanbul Turkey 2003

[8] T Esram andP L Chapman ldquoComparison of photovoltaic arraymaximum power point tracking techniquesrdquo IEEE Transactionson Energy Conversion vol 22 no 2 pp 439ndash449 2007

[9] G M Masters Renewable and Efficient Electric Power SystemsJohn Wiley amp Sons New York NY USA 2004

[10] J L Bernal-Agustın R Dufo-Lopez J A Domınguez-Navarroand J M Yusta-Loyo ldquoOptimal design of a PV-wind system forwater pumpingrdquo in Proceedings of the International Conferenceon Renewable Energies and Power Quality pp 1ndash6 SantanderSpain March 2008

[11] Y Thiaux J Seigneurbieux B Multon H B Ahmed andD Miller ldquoSingle phase AC power load profile emulatorrdquoin Proceedings of the International Conference on RenewableEnergies and Power Quality Santander March 2008

[12] V Courtecuisse J Sprooten B Robyns M Petit B Francoisand J Deuse ldquoA methodology to design a fuzzy logic basedsupervision of hybrid renewable energy systemsrdquo Mathematicsand Computers in Simulation vol 81 no 2 pp 208ndash224 2010

[13] Y Chen and J Wu ldquoAgent-based energy management andcontrol of a grid-connected windsolar hybrid power systemrdquoin Proceedings of the 11th International Conference on ElectricalMachines and Systems (ICEMS rsquo08) pp 2362ndash2365 IEEEWuhan China October 2008

[14] D Das R Esmaili L Xu and D Nichols ldquoAn optimal design ofa grid connected hybrid windphotovoltaicfuel cell system fordistributed energy productionrdquo inProceedings of the 31st AnnualConference of IEEE Industrial Electronics Society (IECON rsquo05)pp 2499ndash2504 November 2005

[15] X Zhu D Xu P Wu G Shen and P Chen ldquoEnergy manage-ment design for a 5kW fuel cell distributed power systemrdquo inProceedings of the 23rd Annual IEEE Applied Power ElectronicsConference and Exposition pp 291ndash297 Austin Tex USAFebruary 2008

[16] K-S Jeong W-Y Lee and C-S Kim ldquoEnergy managementstrategies of a fuel cellbattery hybrid system using fuzzy logicsrdquoJournal of Power Sources vol 145 no 2 pp 319ndash326 2005

[17] Z Jiang ldquoPower management of hybrid photovoltaicmdashfuel cellpower systemsrdquo in Proceedings of the IEEE Power EngineeringSociety General Meeting pp 1ndash6 June 2006

[18] M Nayeripour M Hoseintabar T Niknam and J AdabildquoPower management dynamic modeling and control ofwindFCbattery-bank based hybrid power generation systemfor stand-alone applicationrdquo European Transactions on Electri-cal Power vol 22 no 3 pp 271ndash293 2012

[19] S Harrington and J Dunlop ldquoBattery charge controller char-acteristics in photovoltaic systemsrdquo in Proceedings of the 7th

Annual Battery Conference on Applications and Advances pp15ndash21 Pasadena Calif USA January 1992

[20] S Eren J C Y Hui D To and D Yazdani ldquoA high performancewind-electric battery charging systemrdquo in Proceedings of theCanadian Conference on Electrical and Computer Engineering(CCECE rsquo06) pp 2275ndash2277 Ottawa Canada May 2006

[21] D B Nelson M H Nehrir and CWang ldquoUnit sizing of stand-alone hybrid WindPVfuel cell power generation systemsrdquo inProceedings of the 2005 IEEE Power Engineering Society GeneralMeeting pp 2116ndash2122 San Francisco Calif USA June 2005

[22] R Contino F Iannone S Leva and D Zaninelli ldquoHybridphotovoltaic-fuel cell system controller sizing and dynamicperformance evaluationrdquo in Proceedings of the IEEE PowerEngineering Society GeneralMeeting pp 1ndash6Montreal CanadaOctober 2006

[23] Q Mei W-Y Wu and Z-L Xu ldquoA multi-directional powerconverter for a hybrid renewable energy distributed generationsystem with battery storagerdquo in Proceedings of the CESIEEE 5thInternational Power Electronics and Motion Control Conference(IPEMC 2006) pp 1932ndash1936 Shanghai China August 2006

[24] IHAltas andOOMengi ldquoA fuzzy logic controller for a hybridPVFC green power systemrdquo International Journal of Reasoning-Based Intelligent Systems vol 2 no 3 pp 176ndash183 2010

[25] D Petkovica S ShamshirbandbN BAnuar et al ldquoAn appraisalof wind speed distribution prediction by soft computingmethodologies a comparative studyrdquo Energy Conversion andManagement vol 84 pp 133ndash139 2014

[26] O O Mengi and I H Altas ldquoA fuzzy decision making energymanagement system for a PVWind renewable energy systemrdquoin Proceedings of the International Symposium on Innovations inIntelligent Systems and Applications (INISTA rsquo11) pp 436ndash440IEEE Istanbul Turky June 2011

[27] I Munteanu A I Bratcu and E Ceanga ldquoWind turbulenceused as searching signal for MPPT in variable-speed windenergy conversion systemsrdquoRenewable Energy vol 34 no 1 pp322ndash327 2009

[28] V Galdi A Piccolo and P Siano ldquoExploiting maximum energyfrom variable speed wind power generation systems by using anadaptive Takagi-Sugeno-Kang fuzzy modelrdquo Energy Conversionand Management vol 50 no 2 pp 413ndash421 2009

[29] T Senjyu Y Ochi Y Kikunaga et al ldquoSensor-less maximumpower point tracking control for wind generation system withsquirrel cage induction generatorrdquo Renewable Energy vol 34no 4 pp 994ndash999 2009

[30] M Arifujjaman M T Iqbal and J E Quaicoe ldquoMaximumpower extraction from a small wind turbine emulator using aDC-DC converter controlled by a microcontrollerrdquo in Proceed-ings of the 4th International Conference on Electrical and Com-puter Engineering (ICECE rsquo06) pp 213ndash216 Dhaka BangladeshDecember 2006

[31] V Calderaro V Galdi A Piccolo and P Siano ldquoA fuzzycontroller for maximum energy extraction from variablespeed wind power generation systemsrdquo Electric Power SystemsResearch vol 78 no 6 pp 1109ndash1118 2008

[32] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

[33] V Agarwal R K Aggarwal P Patidar and C Patki ldquoAnovel scheme for rapid tracking of maximum power point inwind energy generation systemsrdquo IEEE Transactions on EnergyConversion vol 25 no 1 pp 228ndash236 2010

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

International Journal of Photoenergy 19

[34] M K Sarıoglu M Gokasan and S Bogosyan AsenkronMakinalar ve Kontrolu Birsen Yayınevi Istanbul Turkey 2003

[35] O O Mengi Yenilenebilir Enerji Sistemlerinde Sureklilik icinAkıllı Bir Enerji Yonetim Sistemi [PhD thesis] KaradenizTechnical University Trabzon Turkey 2011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Carbohydrate Chemistry

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chromatography Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Theoretical ChemistryJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Spectroscopy

Analytical ChemistryInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Quantum Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Organic Chemistry International

ElectrochemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CatalystsJournal of