Comparative Analysis of Maximum Power PointTracking Techniques for PV applications

Ali F Murtaza1,2,Hadeed Ahmed Sher3, Marcello Chiaberge4, Diego Boero2

Mirko De Giuseppe4,Khaled E Addoweesh31Faculty of Engineering, University of Central Punjab, Pakistan

2Department of Mechanical and Aerospace Engineering, Politechnico Politecnico di Torino, Italy3Department of Electrical Engineering, King Saud University, Saudi Arabia

4Department of Electronics and Telecommunication Engineering, Politecnico di Torino, ItalyCorresponding Email:ali faisal355@hotmail.com

Abstract—In the wake of the growing energy requirements theuse of Photovoltaic (PV) systems is increasing around the world.PV systems are not linear in nature therefore special methodsare used to extract the maximum available power. The nonlinear characteristic of the IV curve of PV module has a uniquesingle point of maximum power. In order to operate at thatpower point power electronic converters are used. These powerelectronic converters are controlled with the help of algorithmsknown as Maximum Power Point Tracking (MPPT) techniques.Various unique algorithms are found in the literature with theirspecific advantages and drawbacks. Keeping in view that anefficient tracking of Maximum Power Point (MPP) can increasethe usability of a PV system by many folds, in this paper wepresent a comparison of four widely used MPPT techniquesi.e 1) Fractional Short Circuit Current (FSCC), 2) FractionalOpen Circuit Voltage (FOCV), 3) Perturb & Observe (P&O)and 4) Incremental Conductance (IC) MPPTs are discussed. Thecomparative analysis provides the pros and cons of each MPPTwith a resistive load. The analysis is about the time requiredfor each algorithm to track the maximum power and the systemefficiencies.

Keywords—Solar Photovoltaic, Modeling & Simulation , Incre-mental Conductance, P&O, Fractional Short Circuit Current,Fractional Open circuit voltage, MPPT

I. INTRODUCTION

These days, Renewable energy systems have gained a lotof importance. Several types of renewable energy sources arepracticed around the world the famous among these are solarPV and Wind power generation. These renewable sourceshave an edge on conventional fuel based energy generationbecause they are virtually inexhaustible and do not pose threatsto the environment of the planet. Among all the renewableenergy technologies, solar PV has seen tremendous growthdue to the availability of relatively efficient and cheap PVmodules. The domestic application of PV modules for roof topinstallations have also increased their importance in renewablepower generation. They occupy less space and balance ofsystem (BoS) and therefore, very much suitable for remoteareas. To fulfill the desired power requirements, the solarPV modules are connected in series/parallel configuration.Solar PV modules are primarily based on current sourcewhere the current is produced when light falls on the surface

of solar module. The behavior of PV module is non-linearwith respect to the environment and hence exhibits non-linearIV curves. This means that energy harvesting at maximumefficiency is not simple enough. There exists only one uniquepoint of maximum power and special techniques are requiredto track the point of maximum power. These techniquesare known as Maximum Power Point Tracking techniques(MPPT). These algorithms force the PV system to operate thePV system around the point of maximum power by matchingthe impedance of the load and source. In contrast of the non-linear characteristics, MPPT techniques are vital for any solarPV system. Tens of methods have been reported in literaturefor tracking the maximum power point [1]. Among the 19distinct methods reported by [1], following methods are widelyused by the researchers.

• Perturb & Observe (P&O)• Incremental Conductance• Fractional Open Circuit Voltage• Fractional Short Circuit Current

These methods are widely used since they were first incepted.However, they have some inherited deficiencies despite thefact that they are widely used in the solar PV system. Theresearch community is constantly trying to improve the exist-ing methods with the help of artificial intelligence (AI) basedsystems like Fuzzy, Neural network and ANFIS [2]–[4]. Theuse of such AI techniques fine tune the existing algorithmsbut add complexity to the system design.

In this paper we are presenting a comparison of four basicMPPT techniques. The paper presents the comparison of theseMPPT under the steady as well as varying environmentalconditions. In this way the advantages and drawbacks of thesefour MPPT techniques are studied. The system is modeled andtested in Matlab/Simulink environment.

II. MODELING OF SOLAR PV MODULE

The solar cell is actually a current source that producescurrent when light falls on the surface of the solar cell. Theequivalent model of a PV device can be seen in Fig. 1 [5].The model of an ideal PV cell and the practical PV device canclearly be seen in it. A single PV cell can not produce enough

978-1-4799-3043-2/13/$31.00 2013 IEEE 83

power and can not be used in majority of applications. Inorder to get power that is feasible for most of the applicationsindividual cells are connected in series/parallel configurations.Such configurations are known as PV module. They arecapable of producing output power at the desired levels [6].In literature several models of a PV module are available,however the choice of the single diode based practical modelfor our system is based on the fact that this model has abalanced compromise between accuracy and simplicity [7].The governing equation for a single solar PV cell is given

Fig. 1. Equivalent model of a PV device [5]

below [5]:

I = Ipv − Io(exp(VdnVt

) − 1) (1)

Where,• Ipv is the current generated by incident light• Id is the Schokley diode current and is equal toIo(exp( Vd

nVt) − 1)

• Io is leakage current• Vd is the voltage across the diode• Vt is the thermal voltage of diode and is equal to kT

q• q is the charge on the electron• k is Boltzmann constant• T is temperature of the PN junction in Kelvin

In Fig. 1 two resistances Rp & Rs can be seen that representsthe practical issues of PV device. Here, Rp represents theleakage current to the ground at the borders and Rs models theinternal losses due to current flow of the module. Therefore,eq.1 can be modified in terms of Rp & Rs and becomes;

I = Ipv − Io(exp(V + IRs

nVt) − 1) − V + IRs

Rp(2)

Where,• Ipv is the current of the PV device• V is the voltage of the PV device

Kyocera KC200GT module is used in the modeling of the PVmodule as per the equation 2 [5] and its salient parameters areshown in table I. Figure 2 and 3 show the I-V and P-V curvesof this model respectively.

III. CONVENTIONAL MPPT ALGORITHMS

A. Fractional Short Circuit Current MPPT

Fractional short circuit current (FSCC) is a simple and rapidway of tracking the MPP. It is dependent on PV array but do

Fig. 2. I-V curve of solar PV module used [5]

Fig. 3. P-V curve of PV module [5]

not track the exact MPP, therefore it is not a true MPPT. Thistechnique can be implemented with analog and digital method.This technique works on the principle that the current at MPP(Impp) is approximately equal to the short circuit current Iscby factor k.

Impp∼= kIsc (3)

Where, k is a constant of proportionality that can be calculatedaccording to the PV module data sheet. Typically the valueof k varies from 0.85-0.95. FSCC MPPT can be implementedwith current sensor and is easy to implement. The disadvantageof using this MPPT is the periodic loss of power whilemeasuring the short circuit current. Figure 4 shows the block

TABLE IDATASHEET OF KYOCERA KC200GT MODULE

Imp 7.61 AVmp 26.3 Vpmax 200.143 WIsc 8.21 AVoc 32.9 VKv -0.1230 V/KKi 0.0032 A/KNs 54

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Fig. 4. Block diagram of fractional short circuit MPPT

Fig. 5. Block diagram of fractional open circuit MPPT

diagram for duty cycle calculation using FSCC.

B. Fractional Open Circuit Voltage MPPT

Fractional open circuit voltage (FOCV) is a technique thatworks on the same principle as FSCC. Similar to FSCC itis also not capable of tracking the exact MPP, therefore it isalso not a true MPPT. This technique can be implementedwith analog and digital method. This technique however isconsidered easier to implement than the FSCC. This techniqueworks on the principle that the voltage at MPP (Vmpp) isapproximately equal to the open circuit voltage Voc by factork.

Vmpp∼= kVoc (4)

Where, k is a constant and its value is obtained from the datasheet of the PV module. Generally, its value varies between0.70-0.85. FOCV MPPT needs only one voltage sensor. ThisMPPT also suffers from the periodic loss of power whilemeasuring the open circuit voltage. Figure 5 shows the blockdiagram for duty cycle calculations using FOCV.

C. Perturb and Observe (P&O) MPPT

P&O method is one of the most widely used algorithmfor MPPT. Its mechanism can be seen from the flow chartshown in Fig. 6. In this algorithm the voltage is perturbed inone direction and if the power continues to increase then thealgorithm keeps on perturbing in the same direction. If thenew power is less than the old power, it will perturb in theopposite direction. However this algorithm once reaches theMPP keeps on oscillating around the MPP. Additionally thisalgorithm tends to confuse under rapidly changing weatherconditions. [1].

D. Incremental Conductance (IC) MPPT

Incremental conductance (IC) is a technique that followsthe slope of the power (PV) curve of solar module. Figure7 shows the basic flowchart of this algorithm [8]. The basic

Fig. 6. Basic algorithm of P&O MPPT

rule is very simple that at the maximum power point the slopeof curve is zero. On the right side of the MPP the slope isnegative and on the left side it is positive. Therefore, the rulefor this algorithm is as follows.

Fig. 7. Basic algorithm of incremental conductance MPPT [8] dP/dV = 0 at MPPdP/dV > 0 left of MPPdP/dV < 0 right of MPP

Since the power is a product of voltage and current therefore,we can write

dP

dV=d(IV )

dV= I + V

dI

dV≈ I + V

∆I

∆V(5)

Therefore, the rules can be modified as ∆I/∆V = −I/V at MPP∆I/∆V > −I/V left of MPP∆I/∆V < −I/V right of MPP

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Fig. 8. Simulation setup

Therefore, the tracking of the MPP is accomplished bythe difference of instantaneous conductance (I/V) and theincremental conductance (∆I/∆V ).

The PV module continues to work at the MPP as long asthere is no change in ∆I . This change generally is due tochange in irradiance. The speed of achieving MPP dependson the size of the increment. A small step size will lengthenthe time required to reach the MPP, however it will track theclosest of the ideal MPP. A larger step size will rapidly trackthe MPP but will oscillate around the maximum power point.The complexity of this algorithm is high as compared to othertechniques.

IV. SIMULATION SETUP

A simulation setup is developed in Matlab/Simulink envi-ronment as shown in Fig. 8. All four techniques discussedabove are implemented in this simulation setup. These tech-niques are then tested under the following weather conditionsfor detail analysis

• Steady weather• Dynamic weather

The parameters of simulation setup are given below:• PV Array: A PV array model developed by [5] is used

for simulation. PV arrays contain three strings in paralleland each string contains ten series connected modulesi.e. 3 × 10. Kyocera KC200GT is the module usedwhose electrical characteristics parameters under standardtesting conditions (STC) are shown in Table I. UnderSTC, each PV array can generate 6 kW of power.

• DC-DC converter: Boost converter is used and its com-ponent values are:

– Input Capacitor Cin = 400 µF– Output Capacitor Cout = 10 µF– Inductor L = 15mH

• Resistive load of 80Ω is connected• MPPT Controlling parameters are as follows:

– Both P&O and IC techniques utilize the samplingrate of 5 ms i.e. duration between voltage steps.

– Voc and Isc techniques have discrete PI controllers.Sampling rate of PI controllers is set at 4 ms. BothVoc and Isc techniques measured their respectiveparameters i.e. open-circuit voltage and short-circuitcurrent after time interval of 20 ms.

– All techniques will change the duty cycle (D) of theconverter at a switching frequency of 5 kHz.

V. SIMULATION RESULTS

A. Steady State Weather Conditions

All algorithms are tested under steady weather conditions of1000 W

m2 and 25C. Power curves of techniques are shown inthe upper graph while voltage curves of techniques are shownin the lower graph of Fig. 9. It can be seen from Fig. 9 that

Fig. 9. Performance of MPPT techniques under steady state weatherconditions

IC produces almost negligible power loss oscillations aroundMPP. While, P&O executes power loss oscillations of high

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magnitude but it tracks exact MPP from time to time. However,Voc and Isc techniques are not able to focus the MPP that well.Hence, under steady weather it is evident that IC has the bestperformance.

B. Dynamic Weather Conditions

All techniques are tested under four cases of dynamicweather.

1) Case-1: In this first case, all techniques are allowed tosettle at 500 W

m2 −5C. Then weather conditions are increasedat step rate of 25 W

m2 − 1C per 10msec up to 800 Wm2 − 17C

as shown in the lower graph of Fig. 10, in which leftvertical axis indicates irradiance level while right verticalaxis indicates temperature level. Performance of techniquesis shown in the upper picture of Fig. 10. Under this case,efficiency for each algorithm is measured from 50 ms to 170ms which are as follows: P&O = 99.23%, IC = 99.89%, Voc= 99.7%, Isc = 98.1%. It can be confirmed from the response

Fig. 10. Performance of MPPT techniques under case-1 of dynamic weatherconditions

of algorithms that the response of IC is best under theseconditions. Thus, confirming its efficient performance underfast irradiance changing environment. While, Voc techniquealso performs well but its efficiency is comparatively less thanIC because of periodic measurement of open-circuit voltageduring which PV array power becomes equal to zero. Vocmethod shows good performance because during irradianceand temperature changing, the voltage (Vmpp) of MPP (voltageat which PV array gives maximum power) is not drasticallychanged. Therefore, Voc technique does not have to move farto find out its Vmpp. On the other hand, under these conditions,current (Impp) of MPP (current at which PV array givesmaximum power) is heavily changed. Therefore, Isc work loadgets increased as Impp is continuously changing at a rapid rate.P&O also performs well and the problems associated with

P&O performance during varying weather is not identifiedhere.

2) Case-2: It is the same case as that of Case-1 with theexception that weather conditions are increased at much fasterrate i.e. 25 W

m2 − 1C per 5 ms. Performance of algorithmsand weather conditions can be confirmed from Fig. 11. Underthis case, the efficiency for each algorithm is measured from50 ms to 140 ms which are as follows: P&O = 94.6%, IC= 99.84%, Voc = 99.68%, Isc = 95.07%. It can be evaluated

Fig. 11. Performance of MPPT techniques under case-2 of dynamic weatherconditions

from the efficiencies of algorithms that IC technique holds itsadvantage over other techniques. P&O performance degradedas compared to case-1. Thus under this case, the inability ofP&O to perform well under fast varying weather conditionscan be realized. It is because of the fact that this time weatherconditions are changed at a rate of 5 ms which is also thesampling rate of P&O technique. Voc performs well againdue to the reasons mentioned in case-1. While, Isc methodperformance is also degraded as compared to case-1 becauseof fast varying weather conditions.

3) Case-3: It is the same case as that of Case-2 withthe exception that weather conditions are increased at linear(ramp) rate of 25 W

m2 − 1C per 5msec. Weather conditionscan be confirmed from the lower graph of Fig. 12. Responseof algorithms is shown in the upper graph. Efficiency for eachalgorithm is measured from 50 ms to 140 ms which are asfollows: P&O = 92.15%, IC = 99.82%, Voc = 99.66%, Isc =96.3%. Efficiency of IC and Voc methods is relatively sameas that of case-2. The linearly changing weather conditionsdisturbed P&O further as its efficiency is reduced as comparedto case-2. While, performance of Isc becomes enhanced ascompared to case-2 under these conditions.

4) Case-4: In this case, all algorithms are tested underthe decaying weather conditions. First algorithms are allowedto settle at 800 W

m2 − 17C. Then weather is subjected to

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Fig. 12. Performance of MPPT techniques under case-3 of dynamic weatherconditions

linear decay of 25 Wm2 − 1C per 5 ms up to 500 W

m2 − 5C.Under these decaying conditions, performance of the algo-rithm can be evaluated from Fig. 13. Efficiency for eachalgorithm is measured from 50 ms to 250 ms which are asfollows: P&O = 87.67%, IC = 98.81%, Voc = 99.1%, Isc= 96.5%. Performance of P&O is heavily degraded under

Fig. 13. Performance of MPPT techniques under case-4 of dynamic weatherconditions

these decaying conditions. It loses its path to focus MPPright from the start and retains MPP after quite a while whenconditions get settled at 500 W

m2 − 5C. IC method is ableto perform well but its performance is also compromised abit as compared to rising conditions. However, Voc and Isctechniques are able to perform almost in the similar manner ascompared to rising conditions. All techniques are summarized

TABLE IISUMMARY OF THE COMPARISON OF MPPT TECHNIQUES

Parameters P&O IC FOCV FSCCPrior tuning No No Yes YesDynamic Tracking Reasonable High High MediumSteady Tracking Reasonable High Medium MediumAlgorithm complexity Low High Low LowHardware complexity Low Low Medium MediumSensors V & I V & I V I

in Table II. Where dynamic and steady tracking representsthe tracking of algorithms under dynamic and steady weatherconditions respectively. Control complexity of algorithms canbe evaluated from the algorithm complexity. On the otherhand, the cost of implementation for each algorithm can beevaluated from hardware complexity and sensors.

VI. CONCLUSION

In this paper we have presented a comparison of the fourwidely used MPPT techniques. The simulation study is carriedout using Matlab/Simulink to inspect the performance ofMPPT techniques under steady state as well as the dynamicweather conditions. It has been found that IC is the bestmethod for steady state weather conditions as far as theefficiency and the power losses are concerned. However IC iscomplex in implementation and requires two sensors for properoperation. Among all the four MPPT techniques the IC andFOCV have high convergence speed under dynamic weatherconditions. FSSC and FOCV are the easiest to implement atsoftware level but they require additional hardware arrange-ments for measuring the Isc&Voc. P&O is easy to implementin terms of both embedded software and hardware but is lessefficient under both steady and dynamic weather conditions.The comparison presented in this paper will enrich the researchcommunity and will help in future research in this field.

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