Arab J Sci Eng (2014) 39:4715–4725DOI 10.1007/s13369-014-1056-0

RESEARCH ARTICLE - ELECTRICAL ENGINEERING

A Hybrid Control Method for Maximum Power Point Tracking(MPPT) in Photovoltaic Systems

Mahdi Rajabi Vincheh · Abbas Kargar ·Gholamreza Arab Markadeh

Received: 1 November 2012 / Accepted: 30 April 2013 / Published online: 28 March 2014© King Fahd University of Petroleum and Minerals 2014

Abstract Solar photovoltaic (PV) energy has witnessedgrowth in the past decade. Nowadays, PV energy systemshave proved to be effective methods for renewable energyresources with minimum environmental impacts. Due tothese environmental and economic benefits, PV systems arebeing widely deployed as distributed energy resources in dis-tribution generation systems or microgrids. Maximum powerpoint tracking (MPPT) algorithms have an important role toplay due to optimization performance in these systems. Inthis paper, PV array output voltage has been optimized byincreasing the MPPT algorithm performance. A new hybridfuzzy-neural MPPT controller is proposed. Training data inneural network are optimized by genetic algorithm. The pro-posed controller is simulated and studied using MATLABsoftware. The obtained results show superior capability ofthe suggested method in MPP tracking under rapid fluctua-tion of atmospheric conditions and converter load.

Keywords Boost converter · Fuzzy logic controller ·Genetic algorithm · MPPT · Neural network · PV cell

M. R. Vincheh · A. Kargar (B) · G. A. MarkadehEngineering Faculty, Shahrekord University, Shahrekord, Irane-mail: kargar@ieee.org

M. R. Vinchehe-mail: m.rajabi.vincheh@gmail.com

G. A. Markadehe-mail: arab-gh@eng.sku.ac.ir

AbbreviationsRS Series resistance of a solar cell in �

Rsh Parallel resistance of a solar cell in �

Ic Output current of the PV cell in AVc Output voltage of the PV cell in VIph Photon generated current in AVo Voltage impact of Koc

I Output current of the PV panel in AV Output voltage of the PV panel in VIs Inverse saturation current in An Ideal diode quality coefficientK Boltzmann’s constant (1.38×10−23J/◦ k)ns Number of solar cells connected in seriesn p Number of solar cells (or panels) connected

in parallelq Charge of an electron(1.6×10−19 C)G Irradiation density in W/m2

T Temperature in ◦CIsc Short circuit current in A

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Ksc Temperature coefficient of Isc in 1/ ◦CKoc Temperature coefficient of Voc in V/ ◦ CMPP Maximum power point in WVmpp Voltage at the MPP in VRmpp Resistance at the MPP in �

Varray Output voltage of the PV array in VIarray Output current of the PV array in AParray Output power of the PV array in WVopt Optimized voltage resulted from genetic algorithm

corresponding to Vmpp in VVref Neural network’s output (reference voltage) in V

1 Introduction

Pollutants which are related to PV power generation can onlybe deriving from its parts. Thus, PV systems can be consid-ered as an important energy resource in the future. The begin-ning of using PV systems for electricity generation dates backto the 1970s in the twentieth century and is growing rapidlyall over the world in these years. The efficiency of a PVsystem is basically affected by these three factors: the PVpanel’s efficiency, its inverter efficiency and efficiency of themaximum power point tracking algorithm [1].

For improving the efficiency in the photovoltaic powerconversion, the MPPT process is necessary and tracking algo-rithm has a key role in the MPPT. The MPPT is accomplishedby the PV cell output voltage regulation. In recent years, con-trol algorithm and hardware of the grid connected PV cellsystems have been considered by many researchers [2,3].

Improving the efficiency of the PV panel and inverter isnot an easy task, since it depends on the available technologywhich may need better requirements and will strictly increasethe installation cost. In turn, improvement of the MPPT byusing new control algorithms is easier and cheaper than othersand can be performed by updating these control algorithms.Also, it can rapidly increase the power production of the PVsystem and subsequently decrease its cost.

The operation point of a PV array will be changed byatmospheric conditions and load. Therefore, due to the eco-nomic and efficiency increasing reasons, the operation pointof the PV system must be located within its maximum powerpoint [4]. It is clear that, in the maximum output power pointof the PV array, its internal resistance is equal to the load’sresistance. This equality can be established by changing dutycycle of the DC–DC boost converter of the system.

In these years, several methods have been investigated forMPPT [5]. Also, Weidong et al. in [6] review the latest tech-niques and provide background knowledge about the recentdevelopments in the MPPT techniques.

As an example, [7] puts forward a control algorithm basedon the improved conductance increment method (IncCond)according to the MPPT of photovoltaic cells, because the

traditional one fails to strike a balance well between thephotovoltaic system’s efficiency and accuracy. It employsa less variable step length in the vicinity of the MPP, andproper duty ratio is used to swiftly adjust voltage away fromMPP.

On the basis of the output characteristics of photovoltaicarray, in [8] the principles of incremental conductance andperturb-and-observation algorithms to implement the MPPTin the photovoltaic systems have been analyzed. Softwareflow charts of two methods are presented and advantagesand disadvantages of them are listed in detail.

These methods have some disadvantages such as high cost,difficulty in performance, and instability in operation. Forovercoming these disadvantages, new methods such as arti-ficial neural networks (ANN), have been developed [9,10].In [11,12], improved models of the PV array have beenpresented. These studies have analyzed these models andproposed a genetic algorithm based maximum power pointtracking.

In [13], a two-level neural network-genetic algorithmscheme is employed to estimate the photovoltaic systemmodel. This technique overcomes both power losses causedby oscillation around the MPP in the traditional hill-climbingmethod and difficulty of training data selection in the tradi-tional neural network algorithms.

MPPT controllers using neural network respond quicklyto rapidly changes of solar radiation but must be trained usu-ally with PV-specific data, so [14] proposed a MPPT methodwhich handles both online learning of PV properties andfeed-forward control of the DC–DC converter with a neuralnetwork. A novel GA based method to carry out the MPPTbased on the cell model, also presented in [15].

In [16,17], an improved modeling of the solar PV mod-ule has been analyzed and a GA based MPPT method isproposed. The GA optimized values are used to train an arti-ficial neural network. This method has good performancein the MPPT but many inaccuracies in the neural networktraining.

In [18], a genetic algorithm has been used to improvethe MPPT efficiency of a PV system with induction motordrive by optimizing the input dataset for an ANN modelof the PV modules. A variable frequency volts-per-Hertz(V/f) control method is applied for speed control of theinduction motor, and a space-vector pulse-width modulation(SV-PWM) method is used to operate a three-phase inverter.

The combined MPPT systems which use different solu-tions for increasing speed of the transient response have beenrecently introduced. These methods use particular strategiesfor faster undertaking of the operation point toward the vicin-ity of the MPP rather than other MPPT algorithms [19,20].

A new MPPT method related to incremental conductanceis proposed in [21]. In this method, the I–V panel charac-teristic is divided into two regions with a polynomial linear

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function. One region includes MPP and the other one doesnot. If the operation point locates in the second region, then itwill accelerate toward MPP via larger steps changes. Hence,the MPPT can be achieved rapidly.

The main objective of the present study is to develop acombined MPPT method based on neural networks and fuzzylogic for improving the efficiency of the PV system in therapidly changing atmospheric conditions and load. In thisproposed method, the neural network has been trained bydata that are optimized by GA. This PV system and MPPTalgorithm are simulated using MATLAB software and itsSIMULINK tool boxes.

2 Electrical Model of a PV Cell

Each PV system includes an array containing series and/orparallel PV panels. Each panel contains several PV cells.The equivalent circuit of a PV cell is shown in Fig. 1 and itscurrent–voltage characteristic is expressed as Eq. 1:

IC = Iph − IS

(e

VC +RS IC −VonK T/q − 1

)− VC − IC RS

Rsh(1)

The considered PV array in this article is composed ofthree parallel silicon panels (n p) identical to Solarex SX 3200,and each panel includes 50 series cells (ns). Thus:

I = 3Iph − 3IS

(e

V/50+RS I/3−VonK T/q − 1

)(2)

The produced photon current (Iph) and Vo is as follows:

Iph = G × Isc (1 + Ksc(T − 25)) (3)

Vo = Koc

ns(T − 25) (4)

The irradiation intensity unit is sun and 1 sun is equal to1,000 W/m2.The Solarex SX 3200 panel characteristic whichis given by the manufacturer and presented in Table 1; [22].This model is simulated by MATLAB SIMULINK and PVarray output obtained from an algebraic constraint solver.

Fig. 1 Model of the PV cell

Table 1 Solarex SX 3200 silicon panel characteristics

Is (A) Ks (1/◦C) Koc (V/◦C) Rs (m�) ns

8.7 0.00065 −0.111 8 50

3 The Proposed Fuzzy-Neural Algorithm for MPPT

Although, two familiar fuzzy logic and neural network meth-ods have many important advantages compared with con-ventional methods, they have some disadvantages. One ofthese drawbacks is dependency of them to their parameters.In the MPPT optimization procedure, if the input data arenot included in the training set, then the neural network (fora fixed PV array) is dependent on time, training error andapproximation of non-linear functions. In fuzzy logic methoda tradeoff should be done between the tracking time and sta-tic error. By combining these two methods these problemswill be resolved successfully.

This suggested algorithm is shown in Fig. 2. This algo-rithm divides the P–V characteristic into two regions basedon Fig. 3. In the first region, when the operation point of thePV array is far from the MPP, it can be conducted towardMPP neighborhood (region 2) only by using neural network.The operation point of the PV array in the second region isclose to the MPP, except in the cases when the characteristicof the PV array changes. Thus, by small changes in the PVarray voltage by fuzzy controller, the operation point willbe located exactly on the MPP. The reference voltage forPI control unit in this region is obtained from sum of out-puts of the neural network and the fuzzy logic controllers,Vmpp + �Vmpp.

Flowchart of these two stages is shown in Fig. 4. In thefirst stage, the neural network guides Vref (neural network’soutput) to optimized point of Vopt (optimized voltage resultedfrom genetic algorithm corresponding to Vmpp)which is closeto the MPP.

The training data are identified using GA. In many cases,the inputs data of neural network are temperature and irra-diation, and MPP and the related Vmpp to these inputs areconsidered as the output of neural network. In the presentstudy Vmpp is the output of neural network. In the secondstage, fuzzy logic will be used in order to search the exactMPP. These two stages are repeated alternatively.

Two major defects of the neural network (time depen-dence) and the fuzzy logic (tradeoff) will be resolved bychoosing this algorithm. The tracking speed of this methodis so high, because the start point of the fuzzy logic controlleris close to the MPP. If the PV array characteristic changeswith time, then MPP will be different from the neural net-work output. But this output of neural network in the secondstage will be closed to the new MPP and will be tracked bythe fuzzy logic controller.

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Fig. 2 Proposed controldiagram for MPPT

Fig. 3 P–V characteristic thatis divided into two regions

Fig. 4 Proposed fuzzy-neuralhybrid algorithm

The output of the fuzzy-neural algorithm (Vref) is com-pared to the PV array output (Varray) and the PI controllerhas duty of controlling the PV array output by changing dutycycle of boost converter according to the result of this com-

parison. The output of this controller is compared to a triangu-lar wave by a frequency of 20 KHz, and will produce requiredswitch pulses. This controller along with boost converter hasthe duty of tuning the observed resistance toward the load via

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tuning the output voltage of the PV array. The coefficientsof the PI controller are tuned using Ziegler–Nichols tuningmethod.

3.1 MPPT Optimization by Genetic Algorithm

It should be taken into account that a PV array current isX , and F(X) is the PV array output power that should bemaximized. So, for maximizing the power, firstly the relationbetween arrays power (Parray) and current (Iarray) should beobtained. The relationship between voltage and current ofthe array is:

Varray = ns

⎛⎝Vo − Rs

n pIarray + (nK (T + 273)/q)Ln

×⎛⎝ Iph − Iarray

n p+ Is

Is

⎞⎠

⎞⎠ (5)

and for output power of the array:

Parray = Varray × Iarray (6)

For achieving the objective function, the power should bearranged based on array current:

Parray = ns

⎛⎝Vo − Rs

n pIarray + (nK (T + 273)/q)Ln

×⎛⎝ Iph − Iarray

n p+ Is

Is

⎞⎠

⎞⎠ × Iarray (7)

Current constraint should also be considered:

0 < Iarray < Isc (8)

This objective function is optimized using genetic algorithm(by MATLAB GA tool box). By maximizing this objectivefunction, the optimized amounts of Vmpp and MPP will beobtained in every particular temperature and irradiation.

3.2 Neural Network Structure and Training

Neural networks are the most appropriate choices for approx-imation (modeling) of non-linear systems. For this purpose,in the present study, an attempt was made to use feed for-ward neural network indirectly in controlling MPPT process.The proposed neural network has three layers with temper-ature and irradiation as input variables, and Vmpp as outputvariable.

The performance of neural network depends on used func-tions as transfer function and the way of network training.The neural network should be trained for specific used array,because each PV array has its explicit output characteristics.This output characteristics also change with time and envi-ronmental changes. Therefore, it is essential to train neuralnetwork periodically in order to ensure the accuracy of theMPPT [5].

The proposed neural network has three layers. Each ofthe first two layers has 25 neurons and the third layer has oneneuron. The first and second layers have tangent sigmoid type(Tansing) and the third layer has pure linear type (Purelin)transfer functions. As shown in Fig. 5, training of this neuralnetwork has been done by 220 temperature and irradiationdate series (temperature varies from 5 to 45 ◦C and irradiationvaries from 0.05 to 1 sun) as inputs and 220 correspondingVmpp as outputs (optimized by GA).

3.3 Fuzzy Controller

Change in the PV array output power (�Parray), and changein the PV array output voltage (�Varray) are used as inputs ofthe fuzzy logic controller, and output is the reference voltagechange (�Vmpp). Each of the inputs and output data rangeare considered as three fuzzy sets: positive (P), negative (N),and zero (ZE). The membership functions of these inputsand output are designed by using natural asymmetric PVcharacteristic of P–V panel, and trial and error method asshown in Fig. 6.

The considered rules of the fuzzy logic controller areshown in Table 2. Note that the four first roles are main roles

Fig. 5 MPP and Vmpp outputdata set resulted from GAoptimizing for neural networktraining

12 12.5 13 13.5 14 14.5 15 15.5 16 16.50

100

200

300

400

Vmpp (v)

MPP

(w

)

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Fig. 6 Fuzzy logic inputs andoutput membership functions

Table 2 Fuzzy logic controller rules

Rule number If �Ppv And �Vpv Then �Vref

1 P P P

2 P N N

3 N P N

4 N N P

5 ZE ZE ZE

and show normal operation of the system, and the fifth roleis for stable operation of the system in the MPP.

4 Simulation Results

In the present study, MATLAB software is used for sim-ulating the proposed MPPT algorithm of Fig. 4. After thetraining process, the output of trained network should beso close to optimized output (target) resulted from GA. Asshown in Fig. 7, the training error percent is very small and isnegligible.

After training of the neural network by optimized geneticalgorithm data set, testing data set (temperature and irra-diation) is given to the network and the optimized MPPand Vmpp are predicted in the atmospheric condition of the

testing data. The testing data in the present study are 220 inputdata (temperatures from −5 to 45 ◦C and irradiations from0.025 to 0.975 suns). Given the above test data, it would bepossible to achieve the optimized points resulted from geneticalgorithm. These optimized points provide a pattern for com-paring the neural network response to testing data. Figure 8compares output of the neural network and optimized pointsresulted from genetic algorithm in the testing stage.

As it is clear from Fig. 8, in the testing stage theerrors between target and predicted outputs are not signif-icant. In the following sections various simulated load andatmospheric conditions will be discussed.

4.1 Standard Condition

In standard condition, the irradiation is 1 sun and the environ-ment temperature is 25 ◦C. In this condition and at differentloads, the maximum power point (according to Fig. 9) char-acteristics are:

MPP = 339.2879 W, Vmpp = 14.468 V, Impp = 23.451 A,Rmpp = 0.62 �

As it is clear from Fig. 9, if the load resistance changesfrom the optimum resistance in the MPP (Rmpp) the outputpower of the PV array will be reduced. Thus, the observedresistance from the PV array point of view should become

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Fig. 7 Simulated and targetoutputs (obtained from GA) fortraining data set

12 12.5 13 13.5 14 14.5 15 15.5 16 16.50

100

200

300

400

Vmpp (v)

MPP

(w

)

Target data o

Train data *

Fig. 8 Output of the neuralnetwork and GA optimizedpoints in the testing stage

11 12 13 14 15 16 170

100

200

300

400

Vmpp (v)

MPP

(w

)O Target data

* Test data

Fig. 9 I–V and P–Vcharacteristics in standardcondition and MPP

equal to the Rmpp in order for operation point of the systemlead to the MPP. By tracking the obtained reference voltagefrom fuzzy-neural MPPT algorithm (voltage in MPP), thisgoal can be achieved.

Now, simulation of the proposed MPP tracker in standardcondition will be analyzed. As shown in Fig. 10, control unittracked MPP accurately. In these conditions, output powerhas only 0.1 w ripple from its steady state value (332.28 w),and the time for reaching its steady state (starting time oftracking) is almost 0.6 ms. The starting time of tracking hada remarkable improvement from its reported value (40 ms) in[12].The proposed MPPT algorithm has high efficiency, per-formance and reliability compared to other already reportedPV controls in standard condition and in other conditions isaccurate too.

4.2 Converter Load Changes

For analyzing the load change impacts on the MPP, it isassumed that load resistance and inductance vary as shown

in Fig. 11 in the standard atmospheric condition. In this sit-uation, converter load resistance and inductance decreaseone step in 15 and 35 ms times. As illustrated in Fig. 12,MPP is tracked accurately. This result is obtained from thefact that the aim of the MPPT is to make the converterresistance equal to the optimized MPP resistance from thePV array point of view. This is done by boost converter.Therefore, despite the load changes, it is expected that theobserved equivalent resistance be constant in the converterinput.

4.3 Irradiation Changes

By irradiation change, the characteristic curve of the PV arraychanges and subsequently the maximum power point will bereplaced. If irradiation changes rapidly, then the MPP must betracked quickly and accurately with minimum transient timeand ripple. Figure 13 shows considered irradiation changes.Is this case, operating temperature (25 ◦C) and converter load

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Fig. 10 Accurate and precisetracking of MPP in the standardcondition

Fig. 11 Converter load changesin the standard atmosphericcondition, load 1: R = 4 m �,L = 1 mH, load 2: R = 3 m�,L = 0.66 mH, load 3: R = 2m�, L = 0.54 mH

0 12.5 25 37.5

Load1

Load2

Load3

time (ms)

Con

vert

er lo

ad MPP=339.2879 WVmpp=14.468 VRmpp=0.62 (m ohms)

MPP=339.2879 WVmpp=14.468 VRmpp=0.62 (m ohms)

MPP=339.2879 WVmpp=14.468 VRmpp=0.62 (m ohms)

(4� resistance and 10−3Henry inductance) are constant. Thisfigure also shows MPP, Vmpp and Rmpp in each state.

The simulation results of output power of the PV array inthis situation are shown in Fig. 14. As it is clear from thisfigure, output power of the PV array decreases with decreaseof irradiation. The results show that the amount of trackedpower by proposed algorithm was exactly equal to the opti-mized power in three states of Fig. 13, and tracking speedwith rapidly irradiation changes is highly desirable.

The converter’s output power can be seen in Fig. 15. Tran-sient time of converter’s output power is negligible (almost2 ms).

4.4 Temperature Changes

By changing temperature of the solar cells, PV array charac-teristic curve changes and it will lead to the MPP point shift-

ing. Therefore, with environment temperature changes, theproposed MPP should have sensed this shift in the MPP andtrack it. Figure 16 shows considered temperature changes.Is this case, operating irradiation (1 sun) and converterload (4 � resistance and 10−3 Henry inductance) are con-stant. This figure also shows MPP, Vmpp and Rmpp in eachstate.

The simulation results of output power of the PV arrayin this situation are shown in Fig. 17. Rapid temperaturechanges occur in two times of 25 and 35 ms in this casestudy. As it is shown, output power of the PV array decreasesas temperature increases and vice versa. These results showthat the amount of the tracked power by proposed algorithmis exactly equal to the optimized power and voltages that areshown in Fig. 16. The inverter’s output power can be seen inFig. 18. Transient time of converter’s output power is againnegligible (almost 2 ms).

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Fig. 12 Tracking of the MPPwith converter load changes

Fig. 13 Operation points of theoptimized PV array with rapidirradiation changes

0 15 35 50

0.85

0.9

1

time (ms)

Irra

diat

ion

(sun

)

MPP=239.6164 WVmpp=14.649 VRmpp=0.73 (m ohms)

MPP=309.1158 WVmpp=14.610 VRmpp=0.69 (m ohms)

MPP=339.2879 WVmpp=14.468 VRmpp=0.62 (m ohms)

Fig. 14 Accuracy of the MPPTwith rapid irradiation changes

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Fig. 15 Converter outputpower with rapid irradiationchanges

0 15 35 500

50

100

150

200

250

300

350

time (ms)

Pout

(W

)

Fig. 16 Operation points of theoptimized PV array with rapidtemperature changes

0 15 35 5005

25

45

time (ms)

Tem

oera

ture

(°C

)

MPP=239.6164 WVmpp=14.649 VRmpp=0.73 (m ohms)

MPP=309.1158 WVmpp=14.610 VRmpp=0.69 (m ohms)

MPP=339.2879 WVmpp=14.468 VRmpp=0.62 (m ohms)

Fig. 17 Accuracy of the MPPTwith rapid temperature changes

0 15 35 50100

150

200

250

400

359.8

318.8339.3

time (ms)

Parr

ay (

W)

Fig. 18 Converter outputpower with rapid temperaturechanges

0 15 35 500

50

100

150

200

250

300

350

time (ms)

Pout

(W

)

5 Conclusions

In the present study, a new control method based on fuzzylogic and neural networks is proposed. A PI controller had

the role of converter switching control with regard to the ref-erence voltage. In this process, the genetic algorithm spec-ified the optimized neural network training data (offline)for providing Vmpp in different atmospheric conditions. The

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obtained simulation results demonstrate the appropriate andprecise performance of the compound fuzzy-neural algo-rithm in tracking of the MPP.

The advantages of this compound method can be summa-rized as: optimizing the neural network training by geneticalgorithm, decreasing transient time of the tracking, negligi-ble ripple in the output power in steady state, exact trackingof the MPP with atmospheric conditions changes or con-verter load changes, and finally, growth of the MPPT con-troller efficiency. Drawbacks of the method are: necessity ofexpensive irradiation and temperature sensors, and updatingof the neural network training by changing PV array and othersystem specifications.

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