[IEEE 2010 5th International Conference on Industrial and Information Systems (ICIIS) - Mangalore,...

Maximum Power Point Tracking With Fuzzy Logic Approach for Grid Connected Photovoltaic System

VENKATESHWARLU KOTHA

School of Electrical Sciences, Karunya University Coimbatore, INDIA

Email: [email protected]

Abstract This paper proposes the maximum power tracking from the Photovoltaic system, which is interfaced with utility grid through a three phase inverter, by using a fuzzy logic controller. In this arrangement, three phase step up transformer is used to synchronize the inverter output with the grid ratings. Controller tracks and feeds maximum power to the utility grid. The linguistic variables have been selected appropriately to modulate the firing angle of the inverter for tracking the maximum power. MATLAB/SIMULINK has been used to model fuzzy logic controller. Complete model of the system has been presented in detail. Also, simulation results are presented and described. Keywords: Photovoltaic; Maximum Power Point Tracking; Inverter; Fuzzy Logic Controller (key words)

I. INTRODUCTION As the world's energy demand increases and resources

become scarce, the search for alternative energy resources has become an important issue for our time. With advancements in power electronic technology, the solar Photovoltaic (PV) energy has been recognized as an important natural energy resource [1] because it is clean, abundant and pollution free. As the solar radiation and working cell temperature changes, accordingly the output current, voltage and power from the panel also changes. To track the maximum power from the panel maximum power point tracking controller or maximum power point tracking technique is required [2]. Various approaches have been reported to implement MPPT. The Perturb and Observe (P&O) method [3, 4] needs to calculate dP/dV to determine the maximum power point (MPP). Though it is relatively simple to implement, it cannot track the MPP when the irradiance changes rapidly; and it oscillates around the MPP instead of directly tracking it. The incremental conductance method [5] can track MPP rapidly but increases the complexity of the algorithm, which employs the calculation of dI/dV. The constant voltage method [6], which uses 76% open circuit voltage as the MPP voltage, and the short circuit current method [7] are simple, but they do not always accurately track MPPs.

The conventional methods are having complexity in algorithm and not able track the maximum power effectively in cloudy weather conditions. By using the fuzzy logic controller we can overcome the disadvantages of conventional methods. FLC improves tracking performance [8] when compared with the conventional methods. This method required linguistic rules for maximum power tracking. FLC modulates the firing pulses the power electronic switches used in the inverter.

II. PROPOSED SYSTEM

Fig.1. Block Diagram of the proposed scheme

The block diagram of the proposed scheme is shown in

Fig.1. It consist solar panels for power generation, a three phase inverter, a step-up transformer and the fuzzy logic controller. The generated power from the PV array is fed to three phase inverter through a dc link inductance. The three phase inverter gives the output power from the PV array to the three phase utility grid by using step up transformer.

The FLC is used for modulating the firing pulses for the power electronic switches for that the calculation of error and change in error is needed. For that the FLC needs two inputs, one is the actual power output (Pout) at the output of the transformer and the second is maximum reference power (Pref) which is calculated based on open circuit voltage Voc and short-circuit current Isc of the PV array. It is to be noted that the Voc of the array will vary with temperature and Isc will vary with irradiation. The error is calculated between the actual power and the reference. The step up transformer is used to synchronize the inverter output with utility grid for proper operation of the system.

A. PV Array Model The classical equation of a PV cell describes the relationship between current and voltage of the cell is given in equation as

used in [9]

I=(IPHNP)-(IsNP)[ expqV

NSkTcA-1] (1)

where IS is the saturation current and is given by

IS=IRSTc

TRef

3exp[qEg(

1TRef

-( 1 TC

)/(kA))] (2)

where IRS is the reverse saturation current and is given by

IRS=Isc/[ expqVoc

NS Tc-1] (3)

978-1-4244-6653-5/10/$26.00 2010 IEEE

2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

586

The PV model is developed by equations are used for Simulation in the prop

B. Inverter The inverter circuit used in the paper

Fig.2.If the IGBT switches are numbered athe normal firing sequence is T1 T2, T2 T3, T6, and T6 T1. So, if the load is capable ofthen the direction of power flow can be reverthe dc voltage, the current direction being un

Fig.2. Circuit for three phase fully controlle

III. FUZZY LOGIC CONTROLThe classical controllers are on the basis

systems. Since classical controllers are fixcontrollers, they cannot compensate the parin the plant and cannot adapt changes in thethat we need adaptive controllers. Due to thefuzzy logic controllers over classical contcontroller is used. In a fuzzy logic control snecessity for a plant model. The plant casingle output or multi-input multi-output in ndiagram of fuzzy logic control scheme isFuzzy logic controller is composed offuzzification, inference engine and ddescribed below.

Fig.3. Block Diagram of Fuzzy Logic Co

A. Fuzzification To translate the values of error and cha

fuzzy variable error is assigned with sevecalled negative big (nb), negative mediumsmall (ns), zero (zr), positive small (ps),positand positive big (pb). Similarly change in alpha also assigned with that seven fuzzy vaand change in error (de) are the input varidifference between the reference power (Pr

using the above posed scheme.

r is as shown in as shown in Fig.2 T3 T4, T4 T5, T5

f supplying power, rsed by reversal of

nchanged.

ed inverter

LLER s of simple control xed-gain feedback rameter variations e environment, for e advantages of the trollers, the fuzzy system, there is no an be single input nature.. The block

s shown in Fig.3. f three systems: efuzzification as

ontroller

ange in error in to en fuzzy variables m (nm), negative tive medium (pm), error and change

ariables. Error (e) iables, where e is ref) and the actual

power (Pout) of system. Changesampling interval. The output is the change in firing anglfunctions (trimf) are selectedmembership functions of the error and change in firing anglFig.6, respectively. The range decided by the previous knowparameters. Error is limited betchange in actual power fed maximum power has been fouthe open loop scheme of the pris limited between -3000 and 3is set between -120 and 120 beof an inverter operation is 90output powers can be calculated

Pref=VOCISC

Pout=VdcIdc where Vdc and Id cos

Idc= Iarms3 /2 The error and change in er

follows

e (k) = Pref PO

de = e(k)- e(k-1)

where k is sampling in

Fig.4. Fuzzy Membership

Fig.5. Fuzzy Membership F

e in error de calculated by using from the fuzzy logic controller e, d. Triangular membership d for all these variables. The each variables error, change in le are shown in Fig.4, Fig.5 and of each membership function is

wledge of the proposed scheme tween -2000 and 2000 since the to the grid and the reference

und to be with in this limit from roposed model. Change in error 3000 and change in firing angle ecause the range of firing angle

0. The reference power and the d as follows

(4)

(5)dc are calculated as follows (6)

(7) rror values can be calculated as

(8)

(9)

nstant

Function for FLC Input (e)

Function for FLC Input (de)


587

Fig.6. Fuzzy Membership Function for FLC Output (d)

B. I nference Engine Inference engine performs three tasks: applying fuzzy

operator, applying implication method and aggregating all outputs. Inference engine mainly consists of two sub blocks namely, fuzzy rule base and fuzzy implication. The inputs which are now fuzzified are fed to the inference engine and the rule base is then applied. The output fuzzy sets are then identified using fuzzy implication method. Table.1 shows the rule base of fuzzy logic controller, where all the entries of the matrix are fuzzy sets of error (e), change in error (de) and change in firing angle (d) to the inverter. The commonly used fuzzy implication method is MINMAX and the same is used in this project.. The consequent fuzzy region is restricted to the minimum (MIN) of the predicate truth while selecting output fuzzy set. The output fuzzy region is updated by taking the maximum (MAX) of these minimized fuzzy sets during shaping of output fuzzy space

TABLE I Fuzzy associative memory for proposed FLC based MPPT System

de

e

nb

nm

ns

zr

ps

pm

Pb nb nb nb nb nb nm ns Zr nm nb nb nb nm ns zr Ps ns nb nb nm ns zr ps Pmzr nb nm ns zr ps pm Pb ps nm ns zr ps pm pb Pb pm ns zr ps pm pb pb Pb pb zr ps pm pb pb pb Pb

C. Defuzzification The input for the defuzzification process is a fuzzy set and

the output is a single number. For the final desired output a non-fuzzy value of control, a defuzzification stage is required. In the proposed scheme bisector defuzzification method is used for defuzzification. Here, the actual value of the change in firing angle is calculated by following equation, which partitioned the area under the aggregate fuzzy set into two equal parts as shown in Fig.7.

d dd d d d (10)

Fig.7. Bisector defuzzification method

IV. COMPUTER SIMULATION By using the equations mentioned in section 2.1 the output

for one single solar cell can be obtained. By connecting the solar cells in series and parallel the desired output can be obtained. The simulink model of the solar panel is shown in Fig.8. The simulation is done for constant working cell temperature and constant insolation values

Fig.8. Simulink model for solar cell

To get the desired output of 54volts voltage and 12 A current the parameters and specifications used for simulation are as follows

Voc=21.13 Isc=2; Ns=363;Np=3; TRef=250C;Tc=260C; = 1000 W/m2; Ki=3e-3;Eg=1.14;A=1.3; Rs=8e-3;

For the constant working cell temperature and constant insolation values the output current and output power are constant as shown in Fig.9.

Fig.9. Output current and power from solar panel

The fuzzy logic controller is having two inputs one is error

(e) and the second one is change in error (de) and the output is change in alpha (d). The values e, de can be calculated as

Short CircuitCurrent Temp

Coefficient

54

VoltageInput

2

Short CircuitCurrent

f(u)

SaturationCurrent

f(u)

Reverse Saturation

Current

25

ReferenceeTemparature

Product

PowerOutput

Power

1000

InsolationnInput

Ki

f(u)

Current Output CurrentOutput1

26

Cell Temparature

Input

Iph

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 29

9.2

9.4

9.6

9.8

10

10.2

10.4

10.6

10.8

11

time(sec)

Current(A)

Output Current

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2539

539.2

539.4

539.6

539.8

540

540.2

540.4

540.6

540.8

541

time(sec)

Pow

er(W

)

Output Power


588

mentioned in section 3.1. Here the fuzzy logic controller regulates the firing pulses for switches used in the inverter circuit. The output d is added to the previous value. Here a synchronized six pulse generator is used for generating the six pulses for six switches. The model of fuzzy logic controller is shown in Fig.10.

Fig.10. Implementation of Fuzzy logic controller in

MATLAB/SIMULINK The inverter model used in simulation is as shown in the

Fig.11.The input of the inverter is taken from the solar panel and output of the inverter is given to the three phase step up transformer before feeding to the grid to synchronize with the grid ratings.

Fig.11. Simulink Model of Inverter

By giving proper firing angles to the power electronic switches of inverter and simulating, the output voltages has been obtained as shown inFig.12.

Fig.12. Output Voltage from inverter

The individual models are integrated to get the overall simulink model of proposed scheme. The overall simulation model is shown in Fig.13.

Fig.13. Overall simulation model of FLC for MPPT

By simulating the above system the output is voltages

which are feeding the grid are shown in Fig.14.

Fig.14. Line to Line Voltage from transformer

V. CONCLUSION A simple three phase inverter using power electronic

switches for interfacing photovoltaic arrays with the three-phase utility grid by using step-up transformer has been developed. Simulation of the proposed system is done by using MATLAB/SIMULINK and the output voltage feeding to the grid from the inverter of PV system is obtained and the waves are plotted.

REFERENCES [1] Hiyama Takashi, Kouzuma Shinichi, and Imakubo Tomofui.

Evaluation of neural network based real time maximum power tracking controller for PV system.IEEE Transactions on Energy Conversion, vol.10, No.3, pp. 543548, September 1995.

[2] Ohnishi T, and Takata S. Comparisons of maximum power tracking strategy of solar cell output and control characteristics using step up/down chopper circuit. Transactions of IEEJ, vol.112, No.D (3), pp.250257, May 1992.

[3] Trishan Esram, and Patrick L. Chapman.Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques. IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 22, NO. 2, pp.439-449, JUNE 2007.

[4] Koutroulis E, Kalaitzakis K,and Voulgaris NC. Development of a microcontroller based, photovoltaic maximum power point tracking control system. IEEE Transactions on Power Electronics, vol.16, No.1, pp.4654, January 2001.

[5] Hussein KH,and Muta I. Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. IEEE Proceedings on Generation, Transmission and Distribution, vol.142, No.1, pp.5964, January 1995.

[6] Enslin JHR, Wolf MS, Snyman DB, and Swiegers W. Integrated photovoltaic maximum power point tracking converter. IEEE Transactions on Industrial Electronics, vol.44, No.6, pp.769773, December 1997.

[7] Noguchi T, Togashi S,and Nakamoto R. Short-current pulse-based adaptive maximum-power-point tracking for a photovoltaic power generation system.Electrical Engineering in Japan, vol.139,

[8] N. Ammasai Gounden, Sabitha Ann Peter, Himaja Nallandula, and S. Krithiga. Fuzzy logic controller with MPPT using line-commutated inverter for three-phase grid-connected photovoltaic systems, Renewable Energy (ELSEVIER Journal), vol.34, pp. 909915, July 2008

[9] Huan-Liang Tsai, Ci-Siang Tu, and Yi-Jie Su, Member, IAENG Development of Generalized Photovoltaic Model Using MATLAB/SIMULINK Proceedings of the World Congress on Engineering and Computer Science, October 22 - 24, 2008, San Francisco, USA.

error

change in error

dalpha

alpha

TransportDelay

Scope2

In1 Out1

Pulse

1

Pref

1

Po Fuzzy Logic Controller

Input from solar panel

A

B

C

a

b

c

T hree-PhaseTransformer

(T wo Windings)

T erminator5

T erminator4

Terminator3

Terminator2

T erminator1

Terminator

gm

CE

IGBT6

gm

CE

IGBT5

gm

CE

IGBT4

gm

CE

IGBT3

gm

CE

IGBT 2

gm

CE

IGBT 1

Diode5

Diode4

Diode3

Diode2

Diode1

Diode

0 0.01 0.02 0.03 0.04 0.05 0.06-10

-8

-6

-4

-2

0

2

4

6

8

10

time(sec)

Voltage

(volts)

Inverter output Voltage

Vab

VbcVca

a l

i+ -

i+-

v+-

Voltage Measurement2

v+-

Voltage Mea sureme nt1

v+-

Voltage Measu rement

54

VoltageInput

v+-

Vmca

v+-

Vmbc

v+-

Vmab

v+-

Vdc

Vca

Vbc

Vabc

Vab

A

B

C

Three-Phase Source

A

B

C

a

b

c

Three-PhaseTransf ormer

(Two Windings)

Terminator5

Terminator4

Terminator3

Terminator2

Termin ator1

Terminator

In1Out1

Subsystem2

I n1

I n2

I n3

Out1

Subsystem1

In1

In2

In3

Out1

Out2

Subsystem

Series RLC Branch

Scope9

Scope8

Scope7

Scope2

Scope1 1

Scope

PowerOutpu t

10 00

InsolationnIn put

Ia1

Ia

gm

CE

IGBT6

gm

CE

IGBT5

gm

CE

IGBT4

gm

CE

IGBT3

gm

CE

I GBT2

gm

CE

IGBT1

Fuzzy Logic Controller

7.419

Display6

0.6586

Display5

-12

Display4

164.1

Disp lay3

166.2

Display2

163.8

Disp lay1

In RMS

In RMS

Discrete RMS valu e for

InRMS

Discrete RM S value f1

In RMS

Discrete RMS value f

In RMS

Discrete RMS value

Diode5

Diode4

Diode3

Diode2

Dio de1

Diode

CurrentOutpu t1

i+ -

CurrentMeasuremen t1

s

-+

Cont rolledVol tageSource

0

26

Cell Temparat ure

Input

0 0.01 0.02 0.03 0.04 0.05 0.06-600

-400

-200

0

200

400

600

time(sec)

Voltage

(V)

Output Voltage

VabVbcVca


589

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[IEEE 2010 5th International Conference on Industrial and Information Systems (ICIIS) - Mangalore,...

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