research article a novel maximum power point tracking...

Research ArticleA Novel Maximum Power Point Tracking Algorithm Based onGlowworm Swarm Optimization for Photovoltaic Systems

Wenhui Hou Yi Jin Changan Zhu and Guiqiang Li

School of Engineering Science University of Science and Technology of China Hefei 230026 China

Correspondence should be addressed to Yi Jin jinyi08ustceducn and Changan Zhu changanustceducn

Received 26 January 2016 Revised 1 April 2016 Accepted 6 April 2016

Academic Editor Zbigniew Leonowicz

Copyright copy 2016 Wenhui Hou et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In order to extract the maximum power from PV system the maximum power point tracking (MPPT) technology has alwaysbeen applied in PV system At present various MPPT control methods have been presented The perturb and observe (PampO) andconductance increment methods are the most popular and widely used under the constant irradiance However these methodsexhibit fluctuations among the maximum power point (MPP) In addition the changes of the environmental parameters such ascloud cover plant shelter and the building block will lead to the radiation change and then have a direct effect on the location ofMPP In this paper a feasibleMPPTmethod is proposed to adapt to the variation of the irradianceThis work applies the glowwormswarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system The performanceof the proposed GSO algorithm is evaluated by comparing it with the conventional PampO method in terms of tracking speed andaccuracy by utilizing MATLABSIMULINKThe simulation results demonstrate that the tracking capability of the GSO algorithmis superior to that of the traditional PampO algorithm particularly under low radiance and sudden mutation irradiance conditions

1 Introduction

In the past few decades the worldrsquos energy demand hasrisen steadily with the growth of the population and thechange in peoplersquos lifestyles However traditional fuels arelimited and environmental conditions worsen on a dailybasisThese conditions urge people to seek renewable energySolar energy is a promising renewable energy because of itsvarious advantages such as limitlessness zero production ofpollution and noise and good reliability Hence photovoltaic(PV) power systems have gained increasing attention fromgovernments and researchers in various countries GlobalPV power technology has developed rapidly because ofgovernment support However two major defects hinderits development the high installation cost of the systemand its low photoelectric conversion efficiency at 9ndash17[1] Improving the maximum power tracking technology toextract the highest amount of power from PV systems is oneof the most practical methods to address this problem

The basic concept of maximum power point tracking(MPPT) is to adjust the operating point of a converter up tothemaximum in real time such that the system can constantly

operate at the maximum power point (MPP) In this wayMPPT improves conversion efficiency and reduces powerloss However the MPP changes with the variable externalenvironment thereby further complicating the maximumpower tracking problem [2]

In recent years many MPPT control methods havebeen proposed A detailed description and classification ofnumerous MPPT techniques are made by Subudhi andPradhan [3] Among these methods the most commonlyused approaches are perturb and observe (PampO) [4ndash6] andincremental conductance (IncCon) [7ndash9] A traditional PampOalgorithm does not need to understand the characteristicsof a PV system Thus this algorithm is simple and easyto develop It works by imposing a fixed step perturbationon a reference voltage or current measuring the outputpower of the PV system and comparing the values beforeand after disturbance to determine the direction of thedisturbance for the next step If PV power increases then thedirection of the disturbance is the same as that in the laststep (ie the system is moving toward the MPP) otherwisethe direction is reversed [4] Despite the simple structureof the PampO algorithm the fluctuation among the MPP is

Hindawi Publishing CorporationInternational Journal of PhotoenergyVolume 2016 Article ID 4910862 9 pageshttpdxdoiorg10115520164910862

2 International Journal of Photoenergy

inevitable In the selection of the tracking step it is difficultto take account of both tracking precision and responsespeed In some instances the PampO algorithm generates anerroneous direction when a sudden change in irradianceoccurs By contrast the incremental conductance method isused to determine the disturbance of a control parameterby comparing incremental conductance with instantaneousconductance The tracking performances of four conven-tional methods including the constant voltage method theIncCon method the PampO method and the variable stepsize perturb and observe are compared under fixed solarinsolation by Mohanty et al [9] The results show that theproportion of the respond time between the IncCon methodand the PampO method is about 1 2 However the IncConmethod has the same limitation as the PampO algorithm Toovercome these disadvantages many researchers explored anew direction for MPPT control which combines artificialintelligence and evolutionary computation techniques [2]The main methods for MPPT control are artificial neuralnetworks (ANN) [2 10 11] and fuzzy logic control (FLC)[12 13] AMPPT technique based on the ANNmethodwhichis compared with the traditional PampO algorithm is proposedby Rezk and Hasaneen [11] The simulation results show thatthe tracking speed of the proposed method is faster than thetraditional PampOThe proportion of the respond time is about3 7 at high radiation change rate The comparison betweenthe advanced methods (FLC) and the conventional methods(PampO) is presented by Bendib et al [13] The simulationresults show the performance of the FLCbasedmethod is bet-ter than the conventional methods and the proportion of therespond time between them is about 4 15These methods donot require an accurate mathematical model However theyentail training a large amount of data hence the calculationis complex and a large storage space is necessary In additionmost of the MPPT methods require the expensive currentsensor which increases the inherent cost of the PV systemIn order to reduce costs many MPPT technologies withoutthe current sensor have been proposed over the last decadeA review and summary of some main current sensorlessMPPT technologies are made by Samrat et al [14] With thedevelopment of bionic algorithms more and more scholarsfocused on these algorithms such as genetic algorithmsartificial bee colony algorithm (ABC) and particle swarmoptimization algorithms [15] Many scholars have employedthese bioinspired algorithms for the MPPT of PV systems[16ndash19]

Glowworm swarm optimization (GSO) which is a newtype of bioinspired algorithm shows superior performancein dealing with nonlinear problems although this approachhas yet to be applied in PV systems In this work the GSOalgorithm is used to track the MPP of a PV system To eval-uate the performance of the algorithm the proposed GSO-based MPPT method is implemented on a boost converterand its performance is compared with that of the traditionalPampO algorithm

The information on the PV and boost converter ispresented in Section 2 The proposed algorithm is discussedin Section 3 The simulation of MPPT using SIMULINK and

Iph Ud

Id

Rsh

Ish

RsI

U RL

Figure 1 Equivalent circuit of a PV cell

a discussion of the results are shown in Section 4 Finally theconclusion is presented in Section 5

2 PV and Boost Converter Model

21 PV Equivalent Circuit Model A PV module is used toconvert sunlight into direct current and thus facilitates theconversion of light energy to electric energy A PV-basedsystem can be independently used for streetlights waterpumps and grid connected systems [20]

The typical equivalent circuit model of a PV cell is a singlediode (Figure 1) which consists of a photo current a diode aseries resistor and a parallel shunt resistor

Based on Kirchhoff rsquos current law themathematical equa-tion for the output current of an ideal cell is given by

119868 = 119868ph minus 119868119889 minus 119868sh (1)

where 119868sh is the parallel resistance current and 119868ph is the light-generated current which is proportional to the intensity oflight It can be calculated by

119868ph = [119868sc + 119870119868 (119879119888 minus 119879119903)] sdot 119866 (2)

where 119868sc is the short-circuit current at STC (119879 = 25∘C 119878 =

1000Wm2) and 119870119868 is the short-circuit current temperaturecoefficient of the cell119879119888 and119879119903 are the operating temperatureof the cell and the reference temperature respectively119866 is therelative irradiance coefficient which is calculated by 1198781000(119866 = 1 under nominal condition)

119868119889 is the diode current which according to the Shockleyequation is given by

119868119889 = 119868119874 [exp(119902119880119889

119860119896119879119888

) minus 1] (3)

where 119902 is the electronic charge (119902 = 16 times 10minus19) 119896 is

Boltzmannrsquos constant (119896 = 138 times 10minus23) 119860 is the ideal factorof the diode 119868119874 is the reverse saturation current of the diodeand 119880119889 is the voltage of the equivalent diode According toKirchhoff rsquos voltage law 119880119889 is given by

119880119889 = 119880 + 119868119877119904 (4)

where 119877119904 is the series resistanceThe voltage and current generated by a single PV cell are

very low Thus PV cells are usually connected in series andparallel to achieve the desired power A PVmodule is usually

International Journal of Photoenergy 3

Table 1 Parameters of the PV module type CS6X-305P

Parameter ValueMaximum power (119875max) 305WOpen-circuit voltage (119880oc) 448 VShort-circuit current (119868sc) 897ANumber of cells (119873119904) 72Ideal factor (119860) 13

connected in series by batteries and the series number isgenerally 26 36 54 or 72

The output current equation of the PVmodule is given by

119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1]

minus(119880 + 119868 sdot 119877119904)

119877119901

(5)

where119873119904 is the number of series resistance cells and 119877119901 is theparallel resistance which is very high that its current can beneglected [14] Equation (5) can be simplified as

119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1] (6)

where 119868119874 varies with the change in temperature and is givenby

119868119874 = 119868ro (119879119888

119879119903

)

3

exp[119902119864119892 (1119879119903 minus 1119879119888)

119860119896] (7)

where 119864119892 is the band gap energy of the semiconductor and119868ro is the saturation current of the diode at 25∘C which iscalculated by

119868ro =119868sc

[exp (119902119880oc119860119896119879119903119873119904) minus 1] (8)

where119880oc is the open-circuit voltage of the PVmodule at STC(119879 = 25

∘C 119878 = 1000Wm2)

22 Simulation of the PV Module According to the mathe-matical model of the PV module output power depends ontwo factors namely irradiance and temperature The presentwork focuses on the influence of irradiance (assuming thatthe temperature is 25∘C) The parameters of the PV moduletype CS6X-305P used in the simulation are listed in Table 1The module is composed of 72 solar cells connected in seriesto achieve a maximum power output of 305W

The simulation of the considered PV under variableirradiance via SIMULINK is illustrated in Figure 2

Figures 3 and 4 present the current-voltage (I-V) char-acteristic and power-voltage (P-V) characteristic of the PVmodule respectively The maximum power of the PV isachieved at the extreme point of the P-V curve which isknown as the MPP The maximum power and short-circuitcurrent increase with irradiance

Continuous

Powergui

25

VPV

T

G

PV module

I-V

P-V

Figure 2 PV module with radiance variation

0 10 20 30 40 50 600

1

2

3

4

5

6

7

8

9

10

Module voltage (V)

Mod

ule c

urre

nt (A

)

G = 09

G = 1

G = 075

G = 05

G = 025

Figure 3 I-V characteristic of the PV module under differentradiances

23 Boost Converter and Its Simulation Generally MPPT isused to track theMPPs of PV systemsThe efficiency ofMPPTmainly depends on the MPPT control algorithm and MPPTcircuit The MPPT circuit usually uses a DC-DC converter[21] In the present work a boost converter is used for MPPTto adjust the operating voltage by changing the duty cycle ofthe switch The voltage gain of the converter is calculated by

119866119899 =119880out119880in

=1

1 minus 119863 (9)

where119863 is the duty cycle of the switchFigure 5 shows the subsystem of the boost converter

in SIMULINK Figure 6 indicates that the converter isconnected to the PVmoduleThe input of the boost converteris the output of the PV module The simulation results of thesystem at constant 119863 and variable 119863 are shown in Figures 7and 8 respectively


0 10 20 30 40 50 600

50

100

150

200

250

300

350

Module voltage (V)

Mod

ule p

ower

(W)

G = 09

G = 1

G = 075

G = 05

G = 025

Figure 4 P-V characteristic of the PV module under differentradiances

Figure 7 illustrates the output current and voltage ofthe boost converter at constant 119863 (119863 = 034) Figure 8demonstrates the influence of different input duty cycles onthe output power of a PV systemThe figure indicates that anoptimal duty cycle allows the PV system to function at theMPP

3 Glowworm Swarm Optimization

The GSO algorithm which is a new type of stochastic andmetaheuristic optimization algorithm was first proposed byIndian scholars [22] GSO uses a swarm of glowworms asits agents which are regarded as the potential solutions toa problem The fitness of optimality is measured by theobjective function defined by users In the present workGSO is adopted to generate an optimal reference voltagethat varies with radiance to extract the maximum powerfrom the PV module GSO is an optimization method thatis easy to implement with a rapid convergence speed and fewparameters to adjust

31 Description of the Algorithm TheGSO algorithm is basedon glowworms each of which is considered a potentialsolution to the given objective problem In the first iterationa swarm of glowworms is randomly distributed in a searchspace with an initial luciferin value which determines thebrightness of the glowwormsThe luciferin is updated accord-ing to the objective function value at the current position ofthe glowworm Each glowworm which has its own decisionradius 0 lt 119903

119894

119889lt 119903119904 (119903119904 is the largest sensing radius of

glowworms) seeks a bright individual with high luciferin inits local-decision range and moves toward such individualAfter such move the decision radius of this glowworm isupdated according to the number of optimal individuals in

a decision radius Finally most of the glowworms gather atthe peak point after several iterations Each iteration consistsof a luciferin-update phase a movement phase based on atransition rule and a local-decision range update phase [23]

311 Luciferin-Update Phase Updating luciferin mainlydepends on the objective function value of the currentposition At the same time the decay in luciferin with timeshould be eliminatedThus the formula for updating luciferinis given by

119868119894 (119905 + 1) = (1 minus 120588) lowast 119868119894 (119905) + 120574 lowast 119865 (119909119894 (119905 + 1)) (10)

where 120588 is the luciferin decay constant (0 lt 120588 lt 1) 120574 isthe luciferin enhancement constant 119868119894(119905) and 119868119894(119905 + 1) are theluciferins at iterations 119905 and 119905+1 respectively and 119865(119909119894(119905+1))represents the value of the objective function at agent 119894rsquoslocation at iteration 119905 + 1 In this work objective function 119865is the output power of the PV module which is calculated by

119865 = 119875PV = 119880 lowast 119868 (11)

where the relationship between 119880 and 119868 can be derived from(1)ndash(8) in Section 2 Thus 119865 is the function of 119866 and 119880 119866 isthe system variable and 119880 is the parameter to be optimizedwhich is regarded as the location of the glowworm

312 Movement Phase Each agent is attracted by a superiorindividual but such attraction is limited by the perceptionrange of the individual Thus the neighborhood of the agentshould be first defined by

119873119894 (119905) = 119895 119889119894119895 (119905) lt 119903119894

119889 119868119894 (119905) lt 119868119895 (119905) (12)

119889119894119895(119905) = 119909119894 minus 119909119895 represents the Euclidean distance betweenglowworms 119894 and 119895 at iteration 119905

The probability that glowworm 119894moves toward neighbor119895 is given by

119901119894119895 =

119868119895 (119905) minus 119868119894 (119905)

sum119898isin119873119894(119905)

119868119898 (119905) minus 119868119894 (119905) (13)

The location update formula for this movement can bestated as follows

119909119894 (119905 + 1) = 119909119894 (119905) + 119904 lowast (

119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817

) (14)

where 119904 is the step size and 119909119894(119905) and 119909119894(119905+1) are the locationsat iterations 119905 and 119905 + 1 respectively

313 Local-Decision Range Update Phase If many individu-als with high luciferin values exist in the local-decision rangeof glowworm 119894 the decision radius should be appropriatelyreduced The update formula of the decision radius is

119903119894

119889(119905 + 1)

= min 119903119904max 0 119903119894119889(119905) + 120573 lowast (119899119905 minus

1003816100381610038161003816119873119894 (119905)1003816100381610038161003816)

(15)

where 120573 is the variation coefficient of the decision radiusand 119899119905 is the number of outstanding individuals with highluciferin values in the local-decision range


+

+

+

+

+

+

+

+ +

minus minus

minus

minus

minus

i

i

v

I

U

s

11

2

IPVVPV

v

m

LD

R

m

ka

g DS

C2C1

In 1

Voltage

Current

Scope 2Mosfet

Current

Voltage

D

PWM

control pulses

Figure 5 Subsystem of boost converter

Continuous

Powergui

25

034

1

VPV

PPV

P

IPV

T

D

G

PV module

Boost converter

DC-DCconverter

I-V

P-V

In 1

Figure 6 PV-fed DC-DC boost converter

32 Flowchart of the Algorithm The flowchart of the GSOalgorithm is shown in Figure 9

4 Simulation of MPPT Using SIMULINK andResult Discussion

41 Simulation of MPPT In this work the GSO algorithmis adopted to optimize the reference voltage of the PVsystem under invariant and variant radiances The boostconverter adjusts the duty cycle of the switch according to thereference voltage Finally the output power of the PV systemis controlled The simulation result of the GSO algorithm iscompared with that of the traditional PampO algorithm underthe constant irradiance The structure diagram of the systemis presented in Figure 10

0 0002 0004 0006 0008 001minus2

0

2

4

6

Time (s)

Out

put c

urre

nt (A

)

0 0002 0004 0006 0008 0010

20

40

60

Time (s)

Out

put v

olta

ge (V

)

Figure 7 Output current and voltage of boost converter at constant119863

0 02 04 06 08 10

50

100

150

200

250

300

350MPP

D

Out

put p

ower

(W)

Figure 8 Output power of PV at variable119863


Start

Initialize the swarm of glowworms

Calculate the objective functionUpdate the luciferin

Evaluate the glowworm and find the neighbor muster

Calculate the probability

Select the better glowworm j

Glowworm i moves to j

Update the decision radius

Is the iteration the maximum

Send the best individual

Did irradiance change

No

No

Yes

Yes

t = 1

Figure 9 Flowchart of the GSO algorithm

PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function

Reference voltagePWM

Figure 10 Typical diagram of MPPT using GSO

The proposed GSO algorithm and conventional PampOmethod are simulated using MATLABSIMULINK The PVmodule temperature is considered to be unchanged at 25∘Cduring the simulation Figure 11 presents the simulation of theGSO approach for the considered PV system

Pulse width modulation (PWM) is a technique to cre-ate control pulses for switches PWM is carried out inSIMULINK as shown in Figure 12

The proposed scheme is simulated under two conditionsconstant radiance (including high and low irradiance) andvariable irradiance (mutation in irradiance)

42 Discussion of Results Figure 13 illustrates the outputpower of the PV module for the GSO method and con-ventional method at 1000Wm2 25∘C The figure indicatesthat the tracking efficiency of the proposed GSO algorithmis higher than that of the traditional PampO algorithm The

proportion of the respond time between them is about 1 6Different tracking effects for PampO could be shown whenvarious step sizes are used This work selects a result withsmall and steady oscillations but slow tracking speed

Figure 14 illustrates the output power of the PV modulefor the GSO method and conventional method under lowsolar radiance (300Wm2 25∘C)The result indicates that theGSO algorithm can track the theoretical maximum powerwhereas the conventional PampOmethodhas low efficiency andis not capable of converging to the maximum power

Figure 16 presents the tracking performance of the pro-posed GSO algorithm under varying radiance conditionsThe figure shows the output power of the PV module andboost converter which is coupled with the load The changein irradiance as shown in Figure 15 exhibits suddenmutationat 005 010 015 and 020 s

As shown in Figure 16 the proposed GSO algorithm canaccurately track the MPP of the PV module under variableirradiance The power losses of the system are 243 23232 25 and 241 The output current and outputvoltage of the PV module are shown in Figure 17

5 Conclusion

The MPPT control strategy based on the GSO algorithmis implemented in this work The GSO algorithm is anew type of bioinspired algorithm that is employed forthe maximum power tracking of PV systems The controlmechanism involves optimizing the reference voltage of thePVmodule using the proposed GSO adjusting the operatingvoltage through the boost converter and finally allowing thesystem to work at the MPP The proposed control scheme


ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter

Figure 11 Simulation of GSO approach for the considered PV

1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0

Figure 12 PWM intersect in SIMULINK

0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO

Figure 13 Output power of the PVmodule for theGSOmethod andPampO method under 1000Wm2 25∘C

0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO

Figure 14 Output power of the PV module for the GSO methodand PampO method under 300Wm2 25∘C

is verified using SIMULINK The simulation results indicatethat the scheme can track theMPP under constant irradianceand determine the MPP under changing irradiance Thusminimal power loss occurs after connecting with the loadThe results of the GSO algorithm at constant irradiance arecompared with those of the traditional PampO algorithm Thetracking speed and precision of the proposed method areobviously higher than those of the PampOmethod particularlyat low irradiance Hence the control strategy based on theGSO algorithm can be utilized for the MPPT of PV systems


0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)

Figure 15 Condition of variable radiance

0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV

Figure 16 Output power of the PV and boost converter undervariable irradiance

Nomenclature

119860 Diode ideality factor119889119894119895 Euclidean distance between 119894 and 119895119863 Switch duty cycle119864119892 Band gap energy of semiconductor119865 Objective function119866 Relative radiance coefficient119866119899 Voltage gain of boost converter119868 PV output current (A)119868119889 Diode current (A)119868119894 Luciferin of glowworm 119894

119868119874 Diode reverse saturation current (A)119868ph Light-generated current (A)119868ro Diode reverse saturation current at STC

(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV

Figure 17 Output current and voltage of PV under variableirradiance

119868sc Short-circuit current (A)119868sh Parallel resistance current (A)119896 Boltzmannrsquos constant (JK)119870119868 PV cellrsquos short-circuit current temperature

coefficient119899119905 Number of outstanding individuals119873119894(119905) Neighborhood of agent119873119904 Number of series cells119875119894119895 Probability of glowworm 119894moving to 119895119875max PV maximum power (W)119875PV PV output power (W)119902 Electronic charge (C)119903119894

119889 Local-decision radius

119903119904 Largest sensing radius119877119901 Parallel resistance119877119904 Series resistance119904 Movement step size119878 Solar radiation (Wm2)119905 Iteration number119879119888 Operating temperature (K)119879119903 Reference temperature (K)119880 PV output voltage (V)119880119889 Diode voltage119880in Boost converter input voltage (V)119880out Boost converter output voltage (V)119880oc Open-circuit voltage119909119894 Location of glowworm 119894

Greek Symbols

120573 Variation coefficient of decision radius120574 Luciferin enhancement constant120588 Luciferin decay constant


Competing Interests

The authors declare that they have no competing interests

Acknowledgments

Thestudywas sponsored byNational Basic ResearchProgramof China (973 Program) (2014CB049500) the National Sci-ence Foundation of China (Grant no 51408578) and AnhuiProvincial Natural Science Foundation (1508085QE96 and1508085QE83)

References

[1] M A Eltawil and Z Zhao ldquoMPPT techniques for photovoltaicapplicationsrdquo Renewable and Sustainable Energy Reviews vol25 pp 793ndash813 2013

[2] M F N Tajuddin M S Arif S M Ayob and Z SalamldquoPerturbative methods for maximum power point tracking(MPPT) of photovoltaic (PV) systems a reviewrdquo InternationalJournal of Energy Research vol 39 no 9 pp 1153ndash1178 2015

[3] B Subudhi and R Pradhan ldquoA comparative study onmaximumpower point tracking techniques for photovoltaic power sys-temsrdquo IEEE Transactions on Sustainable Energy vol 4 no 1 pp89ndash98 2013

[4] M A Elgendy B Zahawi and D J Atkinson ldquoAssessment ofperturb and observe MPPT algorithm implementation tech-niques for PV pumping applicationsrdquo IEEE Transactions onSustainable Energy vol 3 no 1 pp 21ndash33 2012

[5] A Pandey N Dasgupta and A K Mukerjee ldquoHigh-performance algorithms for drift avoidance and fast tracking insolar MPPT systemrdquo IEEE Transactions on Energy Conversionvol 23 no 2 pp 681ndash689 2008

[6] A K Abdelsalam A M Massoud S Ahmed and P N EnjetildquoHigh-performance adaptive Perturb and observe MPPT tech-nique for photovoltaic-basedmicrogridsrdquo IEEE Transactions onPower Electronics vol 26 no 4 pp 1010ndash1021 2011

[7] K S Tey and S Mekhilef ldquoModified incremental conductanceMPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation levelrdquo Solar Energy vol 101 pp 333ndash342 2014

[8] S S Mohammed and D Devaraj ldquoSimulation of incrementalconductance MPPT based two phase interleaved boost con-verter using MATLABsimulinkrdquo in Proceedings of the IEEEInternational Conference on Electrical Computer and Commu-nication Technologies (ICECCT rsquo15) pp 1ndash6 Coimbatore IndiaMarch 2015

[9] P Mohanty G Bhuvaneswari R Balasubramanian and NK Dhaliwal ldquoMATLAB based modeling to study the perfor-mance of different MPPT techniques used for solar PV systemunder various operating conditionsrdquoRenewable and SustainableEnergy Reviews vol 38 pp 581ndash593 2014

[10] A K Rai N D Kaushika B Singh and N Agarwal ldquoSimu-lation model of ANN based maximum power point trackingcontroller for solar PV systemrdquo Solar Energy Materials amp SolarCells vol 95 no 2 pp 773ndash778 2011

[11] H Rezk and E-S Hasaneen ldquoA newMATLABSimulinkmodelof triple-junction solar cell and MPPT based on artificialneural networks for photovoltaic energy systemsrdquo Ain ShamsEngineering Journal vol 6 no 3 pp 873ndash881 2015

[12] L Zaghba N Terki A Borni and A Bouchakour ldquoIntelligentcontrolMPPT technique for PVmodule at varying atmosphericconditions using MATLABSIMULINKrdquo in Proceedings of theInternational Renewable and Sustainable Energy Conference(IRSEC rsquo14) pp 661ndash666 Ouarzazate Morocco October 2014

[13] B Bendib H Belmili and F Krim ldquoA survey of the most usedMPPTmethods conventional and advanced algorithms appliedfor photovoltaic systemsrdquo Renewable and Sustainable EnergyReviews vol 45 pp 637ndash648 2015

[14] P S Samrat F F Edwin and W Xiao ldquoReview of currentsensorless maximum power point tracking technologies forphotovoltaic power systemsrdquo in Proceedings of the InternationalConference on Renewable Energy Research and Applications(ICRERA rsquo13) pp 862ndash867 IEEE Madrid Spain October 2013

[15] F Mangiatordi E Pallotti P Del Vecchio and F LecceseldquoPower consumption scheduling for residential buildingsrdquo inProceedings of the 11th International Conference on Environmentand Electrical Engineering (EEEIC rsquo12) pp 926ndash930 VeniceItaly May 2012

[16] C Larbes S M Aıt Cheikh T Obeidi and A ZerguerrasldquoGenetic algorithms optimized fuzzy logic control for the max-imum power point tracking in photovoltaic systemrdquo RenewableEnergy vol 34 no 10 pp 2093ndash2100 2009

[17] M Bechouat Y Soufi M Sedraoui and S Kahla ldquoEnergy stor-age based on maximum power point tracking in photovoltaicsystems a comparison between GAs and PSO approachesrdquoInternational Journal of Hydrogen Energy vol 40 no 39 pp13737ndash13748 2015

[18] T Sudhakar Babu N Rajasekar and K Sangeetha ldquoModifiedparticle swarm optimization technique based maximum powerpoint tracking for uniform and under partial shading condi-tionrdquoApplied Soft Computing Journal vol 34 pp 613ndash624 2015

[19] R B A Koad and A F Zobaa ldquoComparison between theconventional methods and PSO based MPPT algorithm forphotovoltaic systemsrdquo International Journal of Electrical Com-puter Energetic Electronic andCommunication Engineering vol8 no 4 2014

[20] S S Mohammed and D Devaraj ldquoSimulation and analysis ofstand-alone photovoltaic system with boost converter usingMATLABSimulinkrdquo in Proceedings of the International Confer-ence on Circuits Power and Computing Technologies (ICCPCTrsquo14) pp 814ndash821 Nagercoil India March 2014

[21] D Rekioua A Y Achour and T Rekioua ldquoTracking powerphotovoltaic system with sliding mode control strategyrdquo EnergyProcedia vol 36 pp 219ndash230 2013

[22] H Cui J Feng J Guo and T Wang ldquoA novel single multiplica-tive neuron model trained by an improved glowworm swarmoptimization algorithm for time series predictionrdquo Knowledge-Based Systems vol 88 pp 195ndash209 2015

[23] K N Krishnanand and D Ghose ldquoGlowworm swarm opti-mization for simultaneous capture of multiple local optima ofmultimodal functionsrdquo Swarm Intelligence vol 3 no 2 pp 87ndash124 2009

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


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


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


inevitable In the selection of the tracking step it is difficultto take account of both tracking precision and responsespeed In some instances the PampO algorithm generates anerroneous direction when a sudden change in irradianceoccurs By contrast the incremental conductance method isused to determine the disturbance of a control parameterby comparing incremental conductance with instantaneousconductance The tracking performances of four conven-tional methods including the constant voltage method theIncCon method the PampO method and the variable stepsize perturb and observe are compared under fixed solarinsolation by Mohanty et al [9] The results show that theproportion of the respond time between the IncCon methodand the PampO method is about 1 2 However the IncConmethod has the same limitation as the PampO algorithm Toovercome these disadvantages many researchers explored anew direction for MPPT control which combines artificialintelligence and evolutionary computation techniques [2]The main methods for MPPT control are artificial neuralnetworks (ANN) [2 10 11] and fuzzy logic control (FLC)[12 13] AMPPT technique based on the ANNmethodwhichis compared with the traditional PampO algorithm is proposedby Rezk and Hasaneen [11] The simulation results show thatthe tracking speed of the proposed method is faster than thetraditional PampOThe proportion of the respond time is about3 7 at high radiation change rate The comparison betweenthe advanced methods (FLC) and the conventional methods(PampO) is presented by Bendib et al [13] The simulationresults show the performance of the FLCbasedmethod is bet-ter than the conventional methods and the proportion of therespond time between them is about 4 15These methods donot require an accurate mathematical model However theyentail training a large amount of data hence the calculationis complex and a large storage space is necessary In additionmost of the MPPT methods require the expensive currentsensor which increases the inherent cost of the PV systemIn order to reduce costs many MPPT technologies withoutthe current sensor have been proposed over the last decadeA review and summary of some main current sensorlessMPPT technologies are made by Samrat et al [14] With thedevelopment of bionic algorithms more and more scholarsfocused on these algorithms such as genetic algorithmsartificial bee colony algorithm (ABC) and particle swarmoptimization algorithms [15] Many scholars have employedthese bioinspired algorithms for the MPPT of PV systems[16ndash19]

Glowworm swarm optimization (GSO) which is a newtype of bioinspired algorithm shows superior performancein dealing with nonlinear problems although this approachhas yet to be applied in PV systems In this work the GSOalgorithm is used to track the MPP of a PV system To eval-uate the performance of the algorithm the proposed GSO-based MPPT method is implemented on a boost converterand its performance is compared with that of the traditionalPampO algorithm

The information on the PV and boost converter ispresented in Section 2 The proposed algorithm is discussedin Section 3 The simulation of MPPT using SIMULINK and

Iph Ud

Id

Rsh

Ish

RsI

U RL

Figure 1 Equivalent circuit of a PV cell

a discussion of the results are shown in Section 4 Finally theconclusion is presented in Section 5

2 PV and Boost Converter Model

21 PV Equivalent Circuit Model A PV module is used toconvert sunlight into direct current and thus facilitates theconversion of light energy to electric energy A PV-basedsystem can be independently used for streetlights waterpumps and grid connected systems [20]

The typical equivalent circuit model of a PV cell is a singlediode (Figure 1) which consists of a photo current a diode aseries resistor and a parallel shunt resistor

Based on Kirchhoff rsquos current law themathematical equa-tion for the output current of an ideal cell is given by

119868 = 119868ph minus 119868119889 minus 119868sh (1)

where 119868sh is the parallel resistance current and 119868ph is the light-generated current which is proportional to the intensity oflight It can be calculated by

119868ph = [119868sc + 119870119868 (119879119888 minus 119879119903)] sdot 119866 (2)

where 119868sc is the short-circuit current at STC (119879 = 25∘C 119878 =

1000Wm2) and 119870119868 is the short-circuit current temperaturecoefficient of the cell119879119888 and119879119903 are the operating temperatureof the cell and the reference temperature respectively119866 is therelative irradiance coefficient which is calculated by 1198781000(119866 = 1 under nominal condition)

119868119889 is the diode current which according to the Shockleyequation is given by

119868119889 = 119868119874 [exp(119902119880119889

119860119896119879119888

) minus 1] (3)

where 119902 is the electronic charge (119902 = 16 times 10minus19) 119896 is

Boltzmannrsquos constant (119896 = 138 times 10minus23) 119860 is the ideal factorof the diode 119868119874 is the reverse saturation current of the diodeand 119880119889 is the voltage of the equivalent diode According toKirchhoff rsquos voltage law 119880119889 is given by

119880119889 = 119880 + 119868119877119904 (4)

where 119877119904 is the series resistanceThe voltage and current generated by a single PV cell are

very low Thus PV cells are usually connected in series andparallel to achieve the desired power A PVmodule is usually






119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1]

minus(119880 + 119868 sdot 119877119904)

119877119901

(5)


119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1] (6)


119868119874 = 119868ro (119879119888

119879119903

)

3

exp[119902119864119892 (1119879119903 minus 1119879119888)

119860119896] (7)


119868ro =119868sc

[exp (119902119880oc119860119896119879119903119873119904) minus 1] (8)


∘C 119878 = 1000Wm2)




Continuous

Powergui

25

VPV

T

G

PV module

I-V

P-V


0 10 20 30 40 50 600

1

2

3

4

5

6

7

8

9

10

Module voltage (V)

Mod

ule c

urre

nt (A

)

G = 09

G = 1

G = 075

G = 05

G = 025



119866119899 =119880out119880in

=1

1 minus 119863 (9)




0 10 20 30 40 50 600

50

100

150

200

250

300

350

Module voltage (V)

Mod

ule p

ower

(W)

G = 09

G = 1

G = 075

G = 05

G = 025






119894







119865 = 119875PV = 119880 lowast 119868 (11)



119873119894 (119905) = 119895 119889119894119895 (119905) lt 119903119894

119889 119868119894 (119905) lt 119868119895 (119905) (12)



119901119894119895 =

119868119895 (119905) minus 119868119894 (119905)

sum119898isin119873119894(119905)

119868119898 (119905) minus 119868119894 (119905) (13)


119909119894 (119905 + 1) = 119909119894 (119905) + 119904 lowast (

119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817

) (14)



119903119894

119889(119905 + 1)


1003816100381610038161003816119873119894 (119905)1003816100381610038161003816)

(15)



+

+

+

+

+

+

+

+ +

minus minus

minus

minus

minus

i

i

v

I

U

s

11

2

IPVVPV

v

m

LD

R

m

ka

g DS

C2C1

In 1

Voltage

Current

Scope 2Mosfet

Current

Voltage

D

PWM

control pulses


Continuous

Powergui

25

034

1

VPV

PPV

P

IPV

T

D

G

PV module

Boost converter

DC-DCconverter

I-V

P-V

In 1





0 0002 0004 0006 0008 001minus2

0

2

4

6

Time (s)

Out

put c

urre

nt (A

)

0 0002 0004 0006 0008 0010

20

40

60

Time (s)

Out

put v

olta

ge (V

)


0 02 04 06 08 10

50

100

150

200

250

300

350MPP

D

Out

put p

ower

(W)



Start











No

No

Yes

Yes

t = 1


PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function











5 Conclusion



ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of






119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1]

minus(119880 + 119868 sdot 119877119904)

119877119901

(5)


119868 = 119868ph minus 119868119874 [exp(119902 (119880 + 119868 sdot 119877119904)

119860119896119879119888119873119904

) minus 1] (6)


119868119874 = 119868ro (119879119888

119879119903

)

3

exp[119902119864119892 (1119879119903 minus 1119879119888)

119860119896] (7)


119868ro =119868sc

[exp (119902119880oc119860119896119879119903119873119904) minus 1] (8)


∘C 119878 = 1000Wm2)




Continuous

Powergui

25

VPV

T

G

PV module

I-V

P-V


0 10 20 30 40 50 600

1

2

3

4

5

6

7

8

9

10

Module voltage (V)

Mod

ule c

urre

nt (A

)

G = 09

G = 1

G = 075

G = 05

G = 025



119866119899 =119880out119880in

=1

1 minus 119863 (9)




0 10 20 30 40 50 600

50

100

150

200

250

300

350

Module voltage (V)

Mod

ule p

ower

(W)

G = 09

G = 1

G = 075

G = 05

G = 025






119894







119865 = 119875PV = 119880 lowast 119868 (11)



119873119894 (119905) = 119895 119889119894119895 (119905) lt 119903119894

119889 119868119894 (119905) lt 119868119895 (119905) (12)



119901119894119895 =

119868119895 (119905) minus 119868119894 (119905)

sum119898isin119873119894(119905)

119868119898 (119905) minus 119868119894 (119905) (13)


119909119894 (119905 + 1) = 119909119894 (119905) + 119904 lowast (

119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817

) (14)



119903119894

119889(119905 + 1)


1003816100381610038161003816119873119894 (119905)1003816100381610038161003816)

(15)



+

+

+

+

+

+

+

+ +

minus minus

minus

minus

minus

i

i

v

I

U

s

11

2

IPVVPV

v

m

LD

R

m

ka

g DS

C2C1

In 1

Voltage

Current

Scope 2Mosfet

Current

Voltage

D

PWM

control pulses


Continuous

Powergui

25

034

1

VPV

PPV

P

IPV

T

D

G

PV module

Boost converter

DC-DCconverter

I-V

P-V

In 1





0 0002 0004 0006 0008 001minus2

0

2

4

6

Time (s)

Out

put c

urre

nt (A

)

0 0002 0004 0006 0008 0010

20

40

60

Time (s)

Out

put v

olta

ge (V

)


0 02 04 06 08 10

50

100

150

200

250

300

350MPP

D

Out

put p

ower

(W)



Start











No

No

Yes

Yes

t = 1


PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function











5 Conclusion



ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


0 10 20 30 40 50 600

50

100

150

200

250

300

350

Module voltage (V)

Mod

ule p

ower

(W)

G = 09

G = 1

G = 075

G = 05

G = 025






119894







119865 = 119875PV = 119880 lowast 119868 (11)



119873119894 (119905) = 119895 119889119894119895 (119905) lt 119903119894

119889 119868119894 (119905) lt 119868119895 (119905) (12)



119901119894119895 =

119868119895 (119905) minus 119868119894 (119905)

sum119898isin119873119894(119905)

119868119898 (119905) minus 119868119894 (119905) (13)


119909119894 (119905 + 1) = 119909119894 (119905) + 119904 lowast (

119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817119909119895 (119905) minus 119909119894 (119905)

10038171003817100381710038171003817

) (14)



119903119894

119889(119905 + 1)


1003816100381610038161003816119873119894 (119905)1003816100381610038161003816)

(15)



+

+

+

+

+

+

+

+ +

minus minus

minus

minus

minus

i

i

v

I

U

s

11

2

IPVVPV

v

m

LD

R

m

ka

g DS

C2C1

In 1

Voltage

Current

Scope 2Mosfet

Current

Voltage

D

PWM

control pulses


Continuous

Powergui

25

034

1

VPV

PPV

P

IPV

T

D

G

PV module

Boost converter

DC-DCconverter

I-V

P-V

In 1





0 0002 0004 0006 0008 001minus2

0

2

4

6

Time (s)

Out

put c

urre

nt (A

)

0 0002 0004 0006 0008 0010

20

40

60

Time (s)

Out

put v

olta

ge (V

)


0 02 04 06 08 10

50

100

150

200

250

300

350MPP

D

Out

put p

ower

(W)



Start











No

No

Yes

Yes

t = 1


PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function











5 Conclusion



ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


+

+

+

+

+

+

+

+ +

minus minus

minus

minus

minus

i

i

v

I

U

s

11

2

IPVVPV

v

m

LD

R

m

ka

g DS

C2C1

In 1

Voltage

Current

Scope 2Mosfet

Current

Voltage

D

PWM

control pulses


Continuous

Powergui

25

034

1

VPV

PPV

P

IPV

T

D

G

PV module

Boost converter

DC-DCconverter

I-V

P-V

In 1





0 0002 0004 0006 0008 001minus2

0

2

4

6

Time (s)

Out

put c

urre

nt (A

)

0 0002 0004 0006 0008 0010

20

40

60

Time (s)

Out

put v

olta

ge (V

)


0 02 04 06 08 10

50

100

150

200

250

300

350MPP

D

Out

put p

ower

(W)



Start











No

No

Yes

Yes

t = 1


PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function











5 Conclusion



ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Start











No

No

Yes

Yes

t = 1


PVmodule

U

I Boostconverter

Pout Load

G

T

PPV

GSO

Objective function











5 Conclusion



ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


ContinuousPowergui

25

VPV

VPV

VPV

VrefVref

PPV

Pout

P

D

G

P1

V

I

IPV

IPV

T

G

PV module

PWM

GSO algorithm

GSO Boost converter

PWM signal Out 1

Boostconverter


1

1

0

12

VPV

Vref

C

PWM signal

Switch

Constant

Add 1Add

Repeating sequence

minusminus

++

ge0


0 0005 001 0015 002 0025 0030

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

GSOPampO


0 001 002 003 004 0050

20

40

60

80

100

120

140

160

180

200

Time (s)

Out

put p

ower

(W)

GSOPampO




0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


0 005 01 015 02 0250

100200300400500600700800900

10001100

Time (s)

Irra

dian

ce (W

m2)


0 005 01 015 02 0250

50

100

150

200

250

300

350

Time (s)

Out

put p

ower

(W)

Pideal

Pout

PPV


Nomenclature



(A)

0 005 01 015 02 0250

5

10

15

20

25

30

35

40

45

50

Time (s)

Out

put c

urre

ntv

olta

ge o

f PV

mod

ule (

AV

)

IPVVPV






Greek Symbols



Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Competing Interests


Acknowledgments


References

































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of










Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

research article a novel maximum power point tracking...

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