IMPROVEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEM USING ARTIFICIAL NEURAL NETWORK AND GENETIC...

8/10/2019 IMPROVEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEM USING ARTIFICIAL NEURAL NETWORK AND GENETIC

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International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 3, No. 4, Noem!er "#$4

IMPROVEMENTOFGRID-CONNECTED

PHOTOVOLTAICSYSTEMUSINGARTIFICIALNEURALNETWORKAND

GENETICALGORITHMUNDER

DIFFERENTCONDITION

%lire&a 'e&ani$Ma*id +andomar$Ma&iar i&ad!ahsh$and Saeed Vafaei$

$-epartment of lectrical ngineering, Saeh /ranch, Islamic %&ad 0niersit1, Saeh,

Iran

ABSTRACT

Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical

power by converting solar irradiation directly into the electrical energy. In order to control maximum

output power, using maximum power point tracking (PP!) system is highly recommended. !his paper

simulates and controls the photovoltaic source by using artificial neural network ("##) and genetic

algorithm ($") controller. "lso, for tracking the maximum point the "## and $" are used. %ata are

optimi&ed by $" and then these optimum values are used in neural network training. !he simulation results

are presented by using atlab'imulink and show that the neural network$" controller of grid*connected

mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also

it can increase convergence speed to achieve the maximum power point (PP) rather than conventionalmethod. oreover, to control both line voltage and current, a grid side p*+ controller has been applied.

Keywords

ppt neural network genetic algorithm controller Photovoltaic

1. INTRODUCTION

-ue to harmful conse2uences of fossilfuel !urning, for electricit1 production and running out of

them (fossil fuel sources), using from solar energ1 as a clean, inehausti!le and sustaina!le

energ1 source is indispensa!le. 5o6eer, photooltaic (7V) s1stems hae one of the highestpotentials and operating 6a1s for generating electrical po6er !1 conerting solar irradiation

directl1 into the electrical energ1. %lthough, deeloping photooltaic energ1 sources can reduce

fossil fuel dependenc1, 7V panels are lo6energ1 conersion efficient 8$, "9.

In order to control the maimum output po6er, using M77: s1stem is highl1 recommended. %

-Cto-C conerter locates among 7V s1stems and users, 6hich s6itching opration of this

conerter is performed !1 the M77: 839. In the last fe6 decades, different methods are utili&ed in

order to achiee maimum po6er. :he most prealent technics are pertur!ation and o!seration

algorithm (7;9 fu&&1 logic 8?, @9 and %NN 8A, $$9.

%ccording to a!oe mentioned research, the !enefits of pertur!ation and o!seration algorithm

$=


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and incremental conductance are$ lo6 cost implementation " simple algorithm. %nd the

depletion of these methods is ast fluctuation of output po6er around the maimum po6er point

een under stead1 state illumination 6hich results in the loss of aaila!le energ1 8$", $39.

5o6eer the fast ariation of 6eather condition affects the output and these technics cannot trac

the maimum po6er.

0sing fu&&1 logic can sole the t6o mentioned pro!lem dramaticall1. In fact, fu&&1 logic

controller can reduce the oscillations of output po6er around the M77: and has faster respond

than 7;< and IC. Burthermore, conergence speed of this 6a1 is higher than t6o mentioned 6a1.


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Bigure. $. 2uialent circuit of one photooltaic arra1

S s

p #t 7

V ' I V ' II I I e4p $

V n '

+ +=

($)

Ehere, I is the output current,V is the output oltage, Ipis the generated current under a gien

insolation, I#is the diode reerse saturation current, n is the idealit1 factor for a pn *unction, 'sis

the series loss resistance, and 'shis the shunt loss resistance. Vthis no6n as the thermal oltage.

:a!le $ sho6s the Characteristicof red sun A# 6.

:a!le $D 'ed sun A#6 module

3. MPPT ANN and GA

3.1. The Steps ! I"p#e"ent$n% Genet$& A#%'$th"

In order to pursue the optimum point for maimum po6er in an1 enironmental condition, %NN

and +% technic are used. /esides, +% is used for optimum alues and then optimum alues are

used for training %NN 8$A, "$, ""9. :he procedure emplo1ed for implementing genetic algorithm

is as follo6s 8"$9D $. determining the target function ". determining the initial population si&e, 3.

appraising the population using the target function, and 4. conducting conergence test stop if

conergence is proided.

:he target function of +% is applied for its optimi&ation !1 the follo6ingD finding the optimum

FG (F$, F", F3,..., Fn) to determine the B(F) in the maimum alue, 6here the num!er of design

aria!les are regarded as $. F is the design aria!le e2ual to 7V s1stem current and also, B(F) is

the 7V s1stem output po6er that must !e maimi&ed 8"$9. :o determine the target function, the

po6er should !e set !ased on the 7V s1stem current (IF). :he genetic algorithm structures are

presented in :a!le ".

F(F) FB V I= (")

F SC# I I< < (3)

:he current constraint should !e considered too. Eith maimi&ing this function, the optimum

alues for Vmpp and M77 6ill result in an1 particular temperature and irradiance intensit1.

$?

IM7 ( Current at maimum po6er) 4.A4 %

VM7( Voltage at maimum po6er) $@.>=V

7M%F (Maimum po6er) A#EV


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:a!le "D +enetic algorithm structures

Num!er of -esign Varia!le $7opulation si&e "=

Crossoer constant ?=H

Mutation rate $4H

Maimum +enerations $>

(. COM)INATION ! ANN* GA

%NN are most suita!le for the prediction of nonlinear s1stems. Nonlinear s1stems can !e

approimated !1 multila1er neural net6ors and these multila1er net6ors hae !etter result in

comparison 6ith the other algorithm 8$>, $@9. In this paper, feed for6ard neural net6or for

M77: process control is used. :he main part of this method is that, the re2uired data for trainingprocess should !e achieed for each 7V s1stem and each particular position 8$$9. /ased on the

7V characteristic 6hich depend on 7V model and climate change, neural net6or should !e

trained periodicall1. Neural net6or inputs can !e selected as 7V arra1 parameters lie V oc, Iscand

climate data, temperature or !oth of them. :he output is usuall1 one reference signal lie dut1

c1cle or -C lin oltage or Vmpp.

:hree la1ers can !e considered for the proposed %NN. :he input aria!les are temperature and

solar irradiance and Vmpp corresponding to M77 is output aria!le of the neural net6or as

sho6n in Bigure ". %lso, a simple !loc diagram of the 7V s1stem 6ith the proposed M77: is

sho6n in the Bigure 3.

Bigure. ". Beed for6ard neural net6or for M77:

$@


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Bigure. 3. Structure of suggested M77:

:he output of 7V s1stem has aried during time and enironmental conditions. :hus, periodic

training of the %NN is needed. :raining of the %NN is a set of =## data as sho6n in figure 4.

( irradiance !et6een #.#= to $ 6att per s2uare meter (Em") and temperatures !et6een = C to

== C ) and also, a set of =## Vmpp corresponding to M77 is o!tained !1 +% as sho6n in Bigure

=.

Bigure. 4. Inputs data of irradiation and temperature

$A


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Bigure. =. :he outputs of Vmpp Mpp

:o perform of the %NN for M77:, first, the num!er of la1ers, num!er of neurons in each la1er,

transmission function in each la1er and ind of training net6or should !e defined. :he proposed

%NN in this paper has three la1ers 6hich first and second la1ers hae respectiel1 $? and A

neurons and third la1er has $ neuron. :he first and second la1ers of the transfer functions are

:ansig and third la1er is 7urelin. :he training function is :rainlm. :he satisfactor1 sum of s2uares

for the %NN is determined to !e $#A. Ehich training this neural net6or in A=# iterations, 6ill

conerge to a desired target. %fter training, the output of training net6or should !e close to

optimum output from +%. :he neural net6or training 6ith the target data as sho6n in figure >.

% set of @# data is applied for the %NN test. :he neural net6or test 6ith the target data, sho6inga trifling training error percentage a!out #.3H as sho6n in figure?.

>(a)

"#


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>(!)

>(c)

>(d)

"$


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>(e)

Bigure. >. sho6n the output of the neural net6or !1 fallo6ingD (a) :he neural net6or training

6ith the target dataK (!) :he neural net6or of Vmpp 6ith the amount of dataK (c) total error

percentage of the VmppK (d) :he neural net6or of M77 6ith the amount of the target dataK(e)

total error percentage of the M77 data.

?(a)

?(!)

""


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?(c)

?(d)

?(e)

Bigure. ?. sho6n the output of the neural net6or test !1 follo6ingD (a) :he neural net6or test

6ith the target dataK (!) :he neural net6or test of Vmpp 6ith the test target dataK (c) 7ercentageerror of test data VmppK (d) :he neural net6or test of M77 6ith the amount of target dataK (e)

7ercentage error of M77 test data.

+. CONTROL STRATEG, -P*/

% three phase -C%C oltage source inerter (VSI) is used for grid connection ia pulse 6idth

modulation (7EM) technic. /1 appl1ing inerter ia 7EM technic produces high fre2uenc1

harmonics 6hich lead to filter and eliminate the harmonics. :he VSI can pla1 role as an ideal

sinusoidal oltage source.

"3


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S1nchronous reference is calculate 2uantities of dais, 2ais and &ero se2uence in t6o ais

rotational reference ector for three phase sinusoidal signal illustrated in Bigure @. :he e2uations

are gien !1 (4), (=).

Bigure @. :he s1nchronous reference machine

d a d a

2 ! 2 !

# c # c

V V i i

V C V , i C i

V V i i

= =

(4)

d2#

"L "LcosM cos(M ) cos(M )3 3

" "L "LC sinM sin(M ) sin(M )3 33

$ $ $

" " "

+

+

=

(=)

Inerter control model is illustrated in Bigure.A :he goal of controlling the grid side, is eepingthe dc lin oltage in a constant alue regardless of production po6er magnitude. Internal

controlloop 6hich control the grid current and eternal control loop 6hich control the oltage

8"39. %lso, internal controlloop 6hich is responsi!le for po6er 2ualit1 such as lo6 :5- and

improement of po6er 2ualit1 and eternal controlloop is responsi!le for !alancing the po6er.

Bor reactie po6er control, reference oltage 6ill !e set same as dc lin oltage. In grid

connected mode, photooltaic module must suppl1 local needs to decrease po6er from the main

grid.


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37 ( )

"= +

gd d g+ +V I V I (>)

3

N ( )"= g+ d gd +V I V I (?)

If s1nchronous frame is s1nchroni&ed 6ith grid oltage, oltage ector is VGV gdO*# 6hich actie

and reactie po6er ma1 !e as follo6ingD

37

"= gd dV I (@)

3N

"=

gd +V I (A)

Bigure. A. :he inerter control model

0. SIMULATION RESULTS

In this section, simulation results under different terms of operation use 6ith Matla! Simulin is

presented. S1stem !loc diagram is sho6n in Bigure. $#. -etailed model descriptions are gien in%ppendi %.

"=


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Bigure. $#. Case stud1 s1stem

0.1. D$!!e'ent &nd$t$ns ! I''ad$an&e and Te"pe'at'e

:he M77: methods under different conditions of irradiance and temperature in 7V s1stem are

inestigated in this case. :he s1stem is connected to the main grid that includes A#E photooltaic

s1stem and the amount of load is A# E. :here is no po6er echange !et6een photooltaic s1stem

and grid in normal condition.

:he simulation results are descri!ed for ariation insolation leels at constant temperature of

"=C as sho6n in Bigure $$(a). :he output oltage and the current of 7V are depicted in Bigures

$$(!) and $$ (c), respectiel1. Ehen irradiance is increased at tG4 and tG@, it lead to increase in

the output current of 7V as sho6n in Bigure $$(c). :he ealuation of the proposed controller is

compared and anal1&ed 6ith the conentional techni2ue of 7;


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$$(a)

$$(!)

$$(c)

"?


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$$(d)

$$(e)

Bigure $$. Simulated results for 7V (Variation of Irradiance) in case $D (a) IrradianceK (!) Inerter outputoltageK (c) Inerter output currentK (d) 7V po6erK (e) +rid po6er.

$"(a)

"@


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$"(!)

$"(c)

"A


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$"(d)

$"(e)

Bigure $". Simulated results for 7V (Variation of :emperature) in case $D (a) :emperatureK (!) +rid oltageK(c) Inerter output currentK (d) 7V po6erK (e) +rid po6er.

. CONCLUSIONS

:he presented stud1 is a ind of modelling and anal1sis of the 7V s1stem under fault

circumstances !1 using %NN+%. :o etract the maimum po6er from the 7V s1stem %NN+%

techni2ue is used. :he +% !ased offline trained %NN is used to proide the reference oltage

corresponding to the maimum po6er for an1 enironmental changes. :he simulation results3#


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sho6 that using %NN+% controller can dramaticall1 reduce the disadantages of preious

approaches and also, it can decrease oscillations of po6er output around the maimum po6er

point and can increase conergence speed to achiee the maimum po6er point in comparison

6ith 7;< method. In order to control the grid current and oltage, a gridside controller, are

applied. Inerter ad*usts the dc lin oltage and %ctie po6er is fed !1 d ais and reactie po6eris fed !1 2 ais (using 7 control mode). Binall1, !1 appl1ing the appropriate controller, the

photooltaic s1stem in gridconnected mode, can meet the need of load assuredl1.

Append$ A4 Des&'$pt$n ! the Deta$#ed Mde#

7hotooltaic parametersD output po6erG A# E, Carrier fre2uenc1 in V M77:7EM generatorD 4###

5& and in gridsid controllerD >### 5&, !oost conerter parametersD G#.#?5 , CG#.#@? , 7I

coefficients in gridside controllerD PpVdcG #.", iVdcG =, PpIdG A, PiIdG =##, PpI2G A, PiI2G =##

,VgridG ""# No6, t6o cases are inestigated

RE5ERENCES8$9 %.'e&ani, M.+andomar, M.I&ad!ahsh and %.%hmadi,QRnironmentaleconomic scheduling of a

microgrid 6ith rene6a!le energ1 resourcesRR, Journal of Cleaner 7roduction,Vol.@?, pp. "$>"">,"#$=.

8"9 M.I&ad!ahsh, M.+andomar, %.'e&ani and %.%hmadi,QRShortterm resource scheduling of arene6a!le energ1 !ased micro gridRR, 'ene6a!le nerg1,Vol.?=, pp.=A@>#>, "#$=.

839 V. Salas, . "@, "##@.8?9 M.%.S. Masoum , M. Sari ,%n


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8$>9 :. 5i1ama, S. Pou&uma, :. Imau!o, :.5.

IMPROVEMENT OF GRID-CONNECTED PHOTOVOLTAIC SYSTEM USING ARTIFICIAL NEURAL NETWORK AND GENETIC...

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