Intelligent Maximum Power Point Trackers for Photovoltaic

8/10/2019 Intelligent Maximum Power Point Trackers for Photovoltaic

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Intelligent maximum power point trackers for photovoltaicapplications using FPGA chip: A comparative study

F. Chekired a,e, A. Mellit b,a,c,, S.A. Kalogirou d, C. Larbes e

a Development Unit of Solar Equipments (UDES)/EPST-CDER, Bousmail 42000, Algeriab Faculty of Sciences and Technology, Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria

c The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italyd Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603, Cyprus

e Laboratory of Communication Devices and Photovoltaic Conversion, National Polytechnic School of Algiers, Algiers 16200, Algeria

Received 22 July 2013; received in revised form 28 October 2013; accepted 21 December 2013

Available online 10 January 2014

Communicated by: Associate Editor Elias K. Stefanakos

Abstract

In this paper, various intelligent methods (IMs) used in tracking the maximum power point and their possible implementation into areconfigurable field programmable gate array (FPGA) platform are presented and compared. The investigated IMs are neural networks(NN), fuzzy logic (FL), genetic algorithm (GA) and hybrid systems (e.g. neuro-fuzzy or ANFIS and fuzzy logic optimized by geneticalgorithm). Initially, a complete simulation of the photovoltaic system with intelligent MPP tracking controllers using MATLAB/Sim-ulink environment is given. Secondly, the different steps to design and implement the controllers into the FPGA are presented, and thebest controller is tested in real-time co-simulation using FPGA Virtex 5. Finally, a comparative study has been carried out to show the

effectiveness of the developed IMs in terms of accuracy, quick response (rapidity), flexibility, power consumption and simplicity of imple-mentation. Results confirm the good tracking efficiency and rapid response of the different IMs under variable air temperature and solarirradiance conditions; however, the FLGA controller outperforms the other ones. Furthermore, the possibility of implementation of thedesigned controllers into FPGA is demonstrated.2013 Elsevier Ltd. All rights reserved.

Keywords: Photovoltaic system; Intelligent MPPTs; Co-simulation; Real time implementation; FPGA

1. Introduction

Tracking the maximum power point (MPP) of a photo-

voltaic (PV) module/array is an indispensable task of aphotovoltaic control system, since it maximizes the poweroutput of the PV system, and therefore maximizes the PVmodule efficiency. To improve the conversion efficiencyof the electric power generation, a maximum power pointtracking (MPPT) algorithm is usually integrated with the

PV system installations so that the photovoltaic arrays willbe able to deliver the maximum power available under allpossible system-operating conditions.

In the last decade, several researchers have presentedvarious algorithms to track the maximum power of photo-voltaic module/arrays (Reisi et al., 2013).These algorithmsvary in many aspects, such as the number of required sen-sors, cost and complexity, range of effectiveness, conver-gence speed, robustness, correct tracking when irradiationand/or temperature change, and hardware needed for theimplementation.

Artificial intelligence (AI) techniques have been widelyused in photovoltaic applications for modelling, prediction,

0038-092X/$ - see front matter 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.solener.2013.12.026

Corresponding author at: Faculty of Sciences and Technology,Renewable Energy Laboratory, Jijel University, Jijel 18000, Algeria.

E-mail addresses:[email protected],[email protected](A. Mellit).

www.elsevier.com/locate/solener

Available online at www.sciencedirect.com

ScienceDirect

Solar Energy 101 (2014) 8399
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control and optimisation (Mellit and Kalogirou, 2008).Additionally, the use of the intelligent techniques-basedMPP trackers should be noted. These are recently devel-oped and used to improve energy conversion efficiencyunder uniform and non-uniform insolation (Chao and Li,

2010; Kulaksiz and Akkaya, 2012; Salam et al., 2013; Liuet al., 2013; Shaiek et al., 2013; Reisi et al., 2013). As exam-ple, Table 1 summarizes a comparison between a numberof classical algorithms and methods based on AI tech-niques. Generally, MPPT-based AI techniques are moreefficient, have fast response, and are more complex withrespect to the previous methods that are simple, slow,low efficiency and inexpensive. An extensive comparativestudy can be found in Subudhi and Pradhan (2013).

Advances in intelligent techniques embedded into a fieldprogrammable gate array (FPGA) allowed the applicationof such technologies in real engineering problems; however,the application of such technologies in the solar energy field

is still relatively limited. The embedded intelligent algorithminto programmable devices such as FPGA may play a veryimportant role in PV systems, for example in Koutroulis

et al. (2009) and Mekki et al. (2010) intelligent PV emulatorsfor stand-alone PV systems have been developed.

Conventional MPPT methods such as perturb andobserve (P&O), variable step-size P&O, incremental con-ductance (IncCond) and new improved P&O have been

implemented into FPGA due to the simplicity of imple-mentation (Khaehintung et al., 2006; Youssef et al., 2010;Mellit et al., 2011; Chettibi et al., 2012). However, thesealgorithms are not as efficient as the MPPT methods basedon artificial intelligence techniques such as, fuzzy logic,neural network, etc. (Mellit and Kalogirou, 2008; Salamet al., 2013; Liu et al., 2013).

In this work, we are implementing theMPPT system usingreconfigurable FPGAs. This chip offer lower cost implemen-tation since the functions of various components can be inte-grated onto the same FPGA chip as opposedto digital signalprocessor (DSPs), which can perform only DSP-relatedcomputations. In addition, FPGAs can provide equivalent

or higher performance with the customization potential ofApplication Specific Integrated Circuits (ASIC). BecauseFPGAs can be reprogrammed at any time, repairs can be

Nomenclature

Terminology

ANFIS adaptive neuro-fuzzy inference system

ANN artificial neural networkASIC application specific integrated circuitCLBs configurable logic blocksDSP digital signal processorFL fuzzy logicFPGA field programmable gate arrayGA genetic algorithmHDL hardware description languageIncCond incremental conductanceISE integrated software environmentMPP maximum power pointMPPT maximum power point trackingOTP one-time programmableP&O perturb and observePV photovoltaicPWM pulse width modulatorRTL register transfer level

STC standard test conditionsVHSIC very-high-speed integrated circuit

SymbolsD duty cycleE errorG irradiation (W/m2)K Boltzmanns constantm the diode ideality factorP power (W)q the charge of the electron (Cl)T the temperature at standard test conditions (K)Ta air temperature (C)

Greek

l membership functiong efficiencyDE change of the errorE(W)DIL inductor ripple current (A)Dv0 output voltage ripple amplitude (V)

Table 1Comparison between some previous MPPT methods and MPPT-based AI techniques.

Methods Efficiency Complexity level Response/speed Hardware implementation Cost Power consumption

P&O Low Simple Slow Easy Inexpensive LowIncCond Moderate Simple Medium Easy Inexpensive LowOpen circuit voltage Low Simple Medium Easy Inexpensive LowFuzzy logic High High Fast Relatively easy Expensive LowNeural networks High High Fast Difficult Expensive LowGenetic algorithm High Moderate Fast Difficult Expensive Low

84 F. Chekired et al./ Solar Energy 101 (2014) 8399
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performed in situ while the system is running, thus providinga high degree of robustness (Persen, 2004). In some cases,(e.g. in simple applications) the use of a microcontroller isvery suitable. However, some critical mathematic process-ing, such as DSP, would need real-time processing that istime critical. The same applies to advanced control based

on AI techniques. In these situations, FPGAs would be agood solution. FPGAs have a key impact on hardware orsoftware co-design andthey are used as devices for rapid pro-totyping, and for final products.

In our previous research, some intelligent MPPT con-trollers have been designed and implemented into FPGAchip (Chekired et al., 2011, 2012). This paper aims todevelop and verify the effectiveness off our intelligent con-trollers, fuzzy logic (FL), neural networks (NN), adaptiveneuro-fuzzy inference system (ANFIS) and FL-optimizedgenetic algorithm (FLGA) for tracking the MPP in photo-voltaic systems under uniform and rapid variation ofweather conditions.

Initially, Matlab/Simulink is used to simulate and verifythe designed intelligent MPPT controllers. Secondly, a hard-ware description language HDL (VHSIC: very-high-speedintegrated circuits) is employed to design the different partsof the overall system. Finally, the ISE (Integrated SoftwareEnvironment) tools of Xilinx and ModelSim software areused to simulate and implement the designed intelligentMPPTs into an FPGA chip (Virtex-5 ML501- XC5VLX50).Additionally, a comparative study between the above MPPTcontrollers is provided, taking into account the efficiency,implementation complexity, flexibility, power consumptionas well as the response time of each controller.

This paper is organized as follows. Section2presents thedeveloped intelligent MPPT controllers based on; FL,ANN, ANFIS and FLGA. A complete Matlab/Simulinksimulation of the developed MPPT controllers is presentedin Section 3. Section4 gives a detailed description of theprocedure of the implemented controllers into a reconfigu-rable FPGA as well as their hardware co-simulation.Finally, a comparative study is provided in Section5.

2. MPPT-based artificial intelligence controllers

With reference to Fig. 1 the photovoltaic systememployed includes: a photovoltaic module (BP solarMSX 120 Wp), a buck DCDC converter, a MPPT controlunit, a battery and a load (static or dynamic). Maximum

power point tracking means that the photovoltaic moduleor generator is always tuned to operate at its maximumoutput voltage/current rating. An MPP-tracker is a step-down DCDC converter that sets the photovoltaic moduleor generator to operate at MPP independently of the load.Hence, the main function of an MPP-tracker is to adjustthe PV module output voltage to a value in which the PVmodule produces the maximum power output Pmax andtransfer maximum energy to the load. The control unitcontains one intelligent controller to track the MPP. Thesecontrollers are described in the following sections.

2.1. MPPT-based fuzzy logic controller

It has been demonstrated that fuzzy control technique isa viable option to apply in tracking the maximum power ofphotovoltaic systems. Although fuzzy logic controller isefficient in tracking the MPP, it requires good design toselect appropriate fuzzification, inference mechanism, rulebase, and defuzzification processes. The shape of the mem-bership functions associated to the fuzzy logic controller(FLC) linguistic variables are often piecewise linear func-tions (triangular or trapezoidal). The MPPT using theMamdanis FLC approach, which uses the minmax fuzzycombination law is designed in a manner that the control

task try to continuously move the operation point of thePV module as close as possible to the MPP. The two inputsof the FLC are the tracking error (E) and the change of theerror (DE), which are defined as (Messai et al., 2011a):

En Pn Pn 1

Vn Vn 1 1

DEn En En 1 2

PV moduleBP MSX120Wp

DC-DC Buck

converter

Load

Battery

Control unitIntelligent MPPT controller (insideFPGA)

Fig. 1. Schematic block diagram of the considered stand-alone photovoltaic system.

F. Chekired et al. / Solar Energy 101 (2014) 8399 85
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where n is the sampling time, P(n) is the instant power ofthe PV generator and V(n) is the instant correspondingvoltage. These inputs are chosen so that the instant valueofE(n) shows if the load operation power point is locatedon the right or in the left compared to the actual position ofPmax. The DE(n) expresses the moving direction of this

operation point. The output variable is the Pulse WidthModulation (PWM) signal called the duty cycle (D), whichis transmitted to the buck DCDC converter to drive theload. After the rules have been applied, the centre of areais used as the defuzzification method to find the actualvalue of the duty cycle, which is given by the followingequation (Messai et al., 2011a):

D

Pnj1lDj DjPn

j1lDj 3

The following rule bases of the fuzzy sets are used toexpress the inputs and outputs in linguistic variables; PB(positive big), PS (positive small), ZE (zero), NS (negative

small), NB (negative big). Table 2 presents the rules offuzzy controller where the inputs are fuzzy sets of Eandthe DEand the output is the parameter D.

Each variable is described with 5 membership functions,as illustrated in Chekired et al. (2011). The linguistic descrip-tion of the rules is expressed in terms of a knowledge-basedsystem consisting of if . . .thenlinguistic labels and fuzzylogic inference mechanism, such as:

Rule 1 :If E is PB and DEis NB Then D is NB

Rule 2 :If Eis PS and DEis NB Then D is NS

.

.

.

and so forth:

The basic idea is that if the last change in the duty ratio(D) has caused the power to rise, keep moving in the samedirection; otherwise, if it has caused the power to dropmove it in the opposite direction.Fig. 2depicts the surfaceof the fuzzy logic controller. It should be noted that, fuzzylogic methods depend on a careful selection of parameters,definition of membership functions, and fuzzy rules.

2.2. MPPT-based neural network controller

ANNs are widely accepted as a technology offering analternative way to solve complex problems. The developedneural network architecture for the MPPT controller isgiven in Fig. 3a, the input variables are E, DE and theoutput variable is the parameter D. These values are

calculated based on the measured current and voltage fromtheIVcurves at different conditions. The developed struc-ture consists of three layers as:

The input layer consists of two neurons, whose role is totransmit the input values that correspond to the vari-ables (E,DE) to the next layer called the hidden layer.

The hidden layer consists of a number of neurons whoseactivation function is the tangential sigmoid. The num-ber of neurons in the hidden layer was empirically opti-mized during the learning phase by trial-and-error.Various tests carried out have shown that the most accu-rate structure is composed of five neurons.

The output layer has only one neuron representing thecontrol signal D with a linear activation function.

The database employed consists of 1200 patterns ofE,DE and D variables, which has been divided into twosub-databases, 70% of the samples are used to train theANN, and the rest 30% are used to test and validate thenetwork. These patterns were collected using measure-ments of theIVcharacteristics at different solar irradianceand air temperature conditions.

It should be noted that the choice of activation function

in the hidden layer has not been adopted arbitrarily, but it

Table 2Fuzzy logic inference table (Messai et al., 2011a).

DE=E NB NS ZE PS PB

NB ZE ZE PB PB PBNS ZE ZE PS PS PSZE PS ZE ZE NB NSPS NS NS NS ZE ZE

PB NB NB NB ZE ZE

Fig. 2. Surface view created by the FL controller.

Fig. 3a. The employed neural network architecture for the MPPTANNcontroller.



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was chosen after several tests, which showed that the tan-gential sigmoid function converges faster compared to theexponential sigmoid function during the learning phase.The well-known LevenbergMarquardt (LM) algorithmwas used to train the network and the root mean squareerror is used as criterion to stop the training process.

Fig. 3b shows the evolution of the performance error forthe designed ANN-controller. As can be observed, themean squared error during the training process is about105 for five neurons in the hidden layer. The calculatedduty cycle versus the desired one is given in Fig. 3c andas it can be seen a good agreement between ANN-predictedand calculatedDis observed; the relative error is about 1%.

2.3. MPPT-based ANFIS controller

Combining fuzzy logic and neural networks is a power-ful tool in control, prediction and modelling of complexsystems such as photovoltaic systems. MPP tracking using

this technique has been demonstrated in Chekired et al.(2012). Neural networks are based on data training, whilefuzzy logics are based on expert knowledge. When bothdata and knowledge of the underlying system are available,a neuro-fuzzy or adaptive network-based fuzzy inferencesystem (ANFIS) approach is able to exploit both sources

so usually it is more efficient.The designed ANFIS controller has also two inputs(E,DE) and one output (D). The two input variables gener-ate a control signal which is applied to the converter toadjust the duty cycle, so that to ensure the maximisationof the power supplied by the photovoltaic module. TheANFIS controller consists of five layers presented in Che-kired et al. (2012). This controller allows automatic gener-ation of fuzzy rules based on Sugeno inference model:

Rule 1 :If E is A1 and DE is B1 Then D1 fE;DE


.

.

.


where A1, A2. . .A5, B1, B2 . . . B5are the fuzzy sets.The formula of the duty cycle D is given by the follow-

ing equation:

Dw1D1 w2D2 w25D25

w1 w2 w254

where wiare the weights.The variation of the duty cycle Dwith the error Eand

the change of the error DE is shown in the surface plotofFig. 4.Table 3shows the rules table of the ANFIS con-troller where the inputs are fuzzy sets ofEand the changeof error DE, while the output is the parameter D.

2.4. MPPT-based FLGA controller

Developing FLCs need expert knowledge and goodexperimentation in choosing parameters and membershipfunctions. Although the operator skills and knowledge atthe level of the inference rules and the membership func-tions are embedded into the controller, some faults mayappear. To improve the efficiency of the MPPT-based

Fig. 3b. Evolution of the performance error.

0 50 100 1500.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Dutycycle(%)

Time (S)

D desired

D calculated

Fig. 3c. Calculated against desired duty cycle. Fig. 4. Surface view created by the ANFIS controller.

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FLC, Gas are used to find the optimal membership func-tions and parameters as reported in Larbes et al. (2009)and Messai et al. (2011b). This is achieved by implementingthe following stages.

2.4.1. The optimization criterion

The quadratic criterion to be minimized is Larbes et al.(2009):

J Z Pmax P2dt 5

where Pthe desired power and Pmax the maximum powerdelivered by the module under the STC (T= 25 C andG= 1000 W/m2).The aim of this choice is to improve theresponse time and reduce the fluctuations.

2.4.2. Creation of the initial population

In the design of the proposed optimal FLC, two inputs(E, DE) and one output (D) are used. Each variable is

described with five membership functions. The populationconsists of a set of individuals; each individual is composedof three chromosomes: E, DEand D as reported inLarbeset al. (2009).

2.4.3. Optimal FLC membership functions

It can be noted that the GAs have made the system toconverge gradually towards an optimal solution repre-sented by the best individual of the last population. Thisindividual gives the values of the required parameters.The obtained optimal solution gives the shape of the mem-bership functions shown inFig. 5. A 100 individuals pop-ulation has been taken to reach the optimal solution. Thestop criterion is carried out when the maximum number

Table 3ANFIS inference table (Chekired et al., 2012).

DE=E B1 B2 B3 B4 B5

A1 D1 D2 D3 D4 D5A2 D6 D7 D8 D9 D10A3 D11 D12 D13 D14 D15A4 D16 D17 D18 D19 D20

A5 D21 D22 D23 D24 D25

Fig. 5. Optimal FLC membership functions.

Table 4Parameters of the GA.

Parameters Value

Representation BinaryPopulation size 100Generations 50Number of genes Ng 12Rate of crossover 100%

Mutation method Continuous variablesReintegration method ElitistRate of mutation 1/12 = 8.33%Distribution index n= 5



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of generations reaches 50 that is where the fitness functionis at its minimum.Table 4summarizes the GA parameters.

3. Matlab/Simulink-based simulation of the developed

intelligent MPPT controllers

To simulate the behaviour of the designed intelligent

MPPT controllers, the PV system described in Fig.1 is devel-oped under Matlab/Simulink environment (see Fig. 6a). Itconsists of a PV module, a buck DCDC converter, a MPPTcontroller, a battery and a resistive load. The differentelements of the system are modelled as described below.

3.1. PV module modelling

The general electrical circuit of one diode model is usedto model the PV, shown in Fig. 6b. A single diode model isgiven as (Sera et al., 2007):

IIPh I0 eVIRs

Vt 1 V IRsRsh

6where Iph is the photo-generated current in STC, I0 is thedark saturation current in STC, Rsis the panel series resis-tance, Rsh is the panel parallel (shunt) resistance, Vt is thethermal voltage, given by:

VtmKTSTC

q 7

where K is Boltzmanns constant, q is the charge of theelectron,TSTCK is the temperature at standard test condi-tions (STC), and m is the diode ideality factor. These fiveparameters are determined by solving the transcendental

equation (Eq. (6)) using the Newton Raphson algorithmbased only on the datasheet of available parameters. ThePV module specification is reported inAppendix A.

3.2. Buck DCDC converter modelling

The equivalent electric circuit of the employed buckDCDC converter is illustrated inFig. 6c. When the switchin Fig. 6d is closed t 2 [0;DTs], the diode will be reversebiased and a current flows through the inductor into theload (Fig. 6c). As soon as the switch is open t 2 [DTs,Ts],the inductor will maintain the current flow to the load,

but the loop closes through the now forward biased diode(Fig. 6c). Controlling switch position the output voltagecan be maintained at a desired level lower than the inputsource voltage. Thus, the buck converter can be describedby the following set of equations:

diL

dt

mL

L V

i VO RLILL

8

dVc1

dt

iC1

C1Ii IL

C1and

dVC2

dt

iC2

C2IL IO

C29

Rewriting the previous equations in the form of stateequations by taking the inductor current and the capacitorsvoltages as the states of the system, the following stateequations are obtained:

ILcc 2DIL Vi VO RLIL

L DTs 10

Fig. 6a. Matlab/Simulink based simulation of the designed MPPTs for a stand-alone photovoltaic system.

Iph

Id

Rs

Rsh

V

I

Fig. 6b. The equivalent electrical circuit of one diode model.

Fig. 6c. Electrical circuit of a DCDC buck converter.

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Vicc Ii IL

C1DTs and Vocc

IL IoC2

DTs 11

whereILccis the value of the inductor current, Viccand Voccare the values of capacitors voltages. While the output volt-

age ripple amplitude Dmo usually ranges within 1% of theDC component Vo, the amplitude of the inductor currentripple DIL ILmax ILmin varies by as much as 1020% ofits DC value IL (Erickson, 1997). The values of L, C1and C2can be calculated as:

LVi Vo RLIL

2DILDTs 12

C1Ii IL

2DVi DTs and C2

IL Io2DVo

DTs 13

Fig. 6d shows the inductor current in continuousconduction mode. Therefore, the calculated values of the

different components of the converter are: L= 3.5 mH,C1= 5.6 mF, C2= 5.6 mF, Frequency = 20 kHz, Switch(S) = IRFP360, Diode (D) = UF5402.

3.3. Leadacid battery modelling

With regard to the battery modelling, the mathematicalmodel given in Salameh et al. (1992) has been employed.The equivalent electric circuit of the leadacid battery isshown inFig. 6e. The required parameters for this modelare Rbs, Rb1, Rbp, Cb1 and Cbp and which are generally

reported in the datasheet (Lu et al., 1995) Rbs= 0.0013 X,Rb1= 2.84 X, Rbp= 1 K X, Cb1= 2.5 F and Cbp= 4.6501 -KF.Thus, the transfer function of the mathematical modelof the employed battery is given by the following equation

using Matlab/Simulink:

fs 4:2920e5S2 1:3218e8S 1:0003e4

330157100S2 4:6501e7S 1 14

It should be noted that each model is represented by aSimulink block; PV panel: embedded Matlab function isused; Buck converter: Sim Power Systems Simulink is usedto add power elements (diode, capacitor, etc.); Batterymodel: embedded Matlab function is used, and the MPPTcontroller by using Matlab/Simulink block.

3.4. MPPT-controller efficiency

The tracking efficiency (g) is an important decisivefactor of an MPPT algorithm. This value is calculated as:

g

Rt0PMPPTtdtRt0Pmaxtdt

15

wherePMPPTrepresents the output power of the PV systemwith MPPT, and Pmax is the output power at true MPP.

The developed intelligent controllers have been testedunder constant and variable weather conditions (tempera-ture and irradiance). To test their effectiveness, two perfor-mance parameters have been used: the efficiency andresponse time.

3.5. Simulation results

3.5.1. Constant variation of irradiance and temperature

Fig. 7ashows the evolution of the simulated PV powerand duty cycle of the designed controllers versus time atSTC. After a short transitional time (03 s) the FLGAcontroller follows very fast the expected MPP with negligi-ble oscillation. FL and ANN controllers stabilize afterabout 12 s, with few oscillations. However, ANFIS con-troller is the slowest one as it stabilizes after about 16 s.The duty cycle value is stabilized at the value of 0.6, after

approximately 20 s for all controllers. Table 5reports the

Fig. 6d. Inductor current ripple in the buck DC-DC converter.

Ib

Vb

Cb1

Rb1

Rbs

Rbp

Cbp

Voc

Fig. 6e. Electrical circuit of a leadacid battery.



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calculated efficiency, response time and duty cycle rate ofeach technique at STC. With respect to this table, the effi-ciency is about 98% for all techniques, but the GAFL isthe most efficient and converges quickly to the MPP.

3.5.2. Rapid variation of weather conditions

1st case: rapid variation of solar irradiance

In this case, solar irradiance is set to 1000 W/m2 fromt= 0 s to t= 20 s, and 500 W/m2 from t= 25 s tot= 35 s. The period from t = 20 s to t = 25 s is a transitiontime in which the irradiance decrease from 1000 to 500 W/m2. However, the air temperature was kept at 25 C duringthe test period. Simulation results are illustrated inFig. 7b.It can be observed that, the FLGA controller track theMPP after 3 s with negligible oscillations, while the restof controllers tested track the expected MPP after 8 s withfew oscillation around the MPP. However, when the irradi-ance decreases from 1000 to 500 W/m2 during a short per-iod of 5 s, all intelligent controllers developed can detectquickly the new MPP with negligible oscillations. Duringthe simulation period, from 0 to 35 s, the duty cycle follows

well the variation of solar irradiance, except a few

variations in the transition period. The duty cycle changedfrom 58% to 42%, which is due to the variation of solarirradiance. Estimated efficiency, response time and dutycycle rate of each technique under the rapidly changingconditions of irradiance are presented in Table 6. Withrespect to this table, the efficiency is varied between 96%

and 97% for all techniques; the GAFL always performsbetter and converges quickly to the MPP and generally,all controllers exhibit good performance in the case ofrapid variation of irradiance.

2nd case: rapid variation of air temperature

In this case, air temperature is set to 45 C fromt = 0 stot = 20 s, and 25 C fromt = 25 s tot = 35 s. The periodfrom t= 20 s to t= 25 s is a transition time in which thetemperature decrease from 45 to 25C. However, thesolar irradiance was kept at 1000 W/m2 during the testperiod. Simulation results are presented in Fig. 7(c). Asobserved, the FLGA controller track very fast theMPP after 3 s,while the rest of controllers track theexpected MPP after 8 s with a few oscillations aroundthe MPP. The expected power is about 110 W due to theinfluence of the temperature (45 C). However, when theair temperature decrease from 45 C to 25 C the devel-oped intelligent algorithm again track quickly the newexpected MPP (120 W) with a few oscillations. The dutycycle also exhibits few variations at the transition period,which is negligible. Efficiency, response time and dutycycle rate of each technique in the rapidly changing condi-tions of temperature are presented in Table 7. As can beseen, the efficiency varied between 97% and 98% for alltechniques and the controllers are not influenced by this

variation in temperature.

0 2 4 6 8 10 12 14 16 18 200

20

40

60

80

100

120

Modulepower(W)

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

DudycycleD

Time (S)

Neural networks

ANFIS

Fuzzy optimized by GA

Fuzzy logic

Fig. 7a. PV power evolution of the simulated MPPT and its corresponding duty cycleD during a test period (from 0 to 20 s) at STC.

Table 5Efficiency, response time and duty cycle rate of each technique in STC.

MPPT-controller Efficiencyg(%)

Responsetime (s)

Duty cyclerate D (%)

FL 98.12 12.03 58.11FLGA 98.72 3.03 59.8

ANFIS 98.33 15.88 59.44ANN 98.43 11.96 59.55

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4. Co-simulation and hardware implementation of intelligent

MPPT controllers

The different steps to realize and simulate a project intoFPGA are summarized as follows:

Write a program in HDL (VHDL, Verilog, schemat-ics) or use System Generator (Matlab/Simulink).

Simulate (using ModelSim, or co-simulation withMatlab/Simulink).

Synthesis (Develop the RTL schematic and technol-ogy, etc.).

Place and route (place and route of the designedproject, estimate the consumption power, pins, etc.).

Timing, maximum clock rate is determined by tools. Generating the bit-stream into the FPGA. Configure the target device and download it into a

FPGA.

ModelSim, Matlab/Simulink and ISE software are used

for this subject.

4.1. FPGAs

FPGAs are programmable semiconductor devices thatare based around a matrix of configurable logic blocks(CLBs) connected through programmable interconnections(Xilinx, Inc.). The following are some of the most impor-tant advantages of FPGAs, which motivated us to imple-ment intelligent MPPT controllers:

Programmed and reprogrammed many times and

consolidation of multiple components into a singlecomponent.

Eliminating the costs associated with re-design ormanually updating electronic systems and higherspeed compared with Microcontroller or DSP.

Much faster than software-based logic andimproved design update and enhancement options.

Relatively lower implementation costs; however,microcontrollers are low-cost and powerconsumption.

Flexible, it means that you can add subtract thefunctionality as required, this cannot be done in

microcontroller.

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

Dudyc

ycleD

Time (S)

0 5 10 15 20 25 30 35

0

20

40

60

80

100

120

Modulepower(W

)

0 5 10 15 20 25 30 35

600

800

1000

S(W/m2)

Neural networks

Fuzzy logic


ANFIS

Fig. 7b. PV power evolution of the simulated MPPT and its corresponding duty cycleD in rapid variation of irradiance (from 1000 W/m2 to 500 W/m2

during a transition time of 5 s), air temperature was kept at 25 C.

Table 6Efficiency, response time and duty cycle rate of each technique in rapidly

changing conditions of irradiance.MPPT-controller Efficiency

g(%)Responsetime (s)

Duty cyclerate D (%)

FL 96.4 8.15 42.98FLGA 97 3.12 43.07ANFIS 96.25 7.92 42.32ANN 96.66 7.84 42.87



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Concurrent, its means that you can take sequentialfunctionality like adding soft processor core. Whilethe microcontroller as always sequential. This makesFPGAs better suited for real-time applications suchas executing DSP algorithms.

FPGA is used mainly for programmable logic butmicrocontroller is mainly for hard-core processing.

4.2. VHDL code implementation

VHDL stands for VHSIC hardware description lan-guage. The language is formally defined by IEEE Standard1076. The standard was ratified in 1987 (referred to asVHDL 87), and revised several times. This paper mainlyfollows the revision in 1993 (referred to as VHDL 93).VHDL is intended for describing and modelling a digitalsystem at various levels and is an extremely complex lan-guage (Pong, 2008). Implanting a VHDL code is princi-pally a two-step process, i.e., synthesis and placement-

and-routing (Ruelland et al., 2003) described below:

Synthesis

Synthesis involves compiling the VHDL code withtools (e.g. Xilinx Foundation ISE 11.1i) which is a com-

mercially available tool. The result of this compilation isa flip-flop and logic function transcription of thehigh-level functionalities. Some functions can be resolvedin different ways, depending on the target component.VHDL codes can be simulated using ModelSim Xilinxor other tools.

Placement-and-Routing

The result of the Placement-and-Routing is the finalcode to be implanted on the FPGA. An auxiliary result isthe VHDL file giving the operation of the implanted code

and taking the propagation times of the target device intoaccount. This file can be used in co-simulation and thisresult in a representation of a virtual prototype. This allowschecking that the Placement-and-Routing has notaltered the performance and that the synchronization ofall signals is compatible with the propagation times.

4.3. The designed VHDL-modules

The controllers have been designed using the VHDL,integrated with the Xilinx foundation ISE11.1i tools(Xilinx, Inc.). The different designed VHDL-modules can

be summarized to main and secondary blocks, as:

0 5 10 15 20 25 30 350

0.2

0.4

0.6

Dud

ycycleD

Time (S)

0 5 10 15 20 25 30 350

20

40

60

80

100

120

Modulepower(W)

0 5 10 15 20 25 30 35

300

310320

T(K)

Neural networks

Fuzzy logic


ANFIS

Fig. 7c. PV power evolution of the simulated MPPT and its corresponding duty cycleD in rapid variation of air temperature (from 45 C to 20 C duringa transition time of 5 s), solar irradiance was kept at 1000 W/m2.

Table 7Efficiency, response time and duty cycle rate of each technique in rapidlychanging conditions of air temperature.

MPPT-controller Efficiency

g(%)

Response

time (s)

Duty cycle

rate D (%)FL 98.07 8.23 58.8FLGA 98.20 2.97 58.67ANFIS 97.52 7.87 58.9ANN 97.86 7.75 58.93

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4.3.1. Main block

MPPT algorithm

The MPPT algorithm is the main block; it represents theVHDL-module to be tested. It corresponds to one intelli-

gent MPPT controller (ANN, FL, etc.). As an example,Figs. 8a8c depict the RTL (Register Transfer Level) ofthe designed controllers; FLCGA, neuro-fuzzy (ANFIS)and NN controller, respectively; the FL controller haspractically similar structure as FLGA controller.

4.3.2. Secondary blocks

Frequency divider

This VHDL-module generates a clock signal at 1 Hzfrom the internal clock signal of 100 MHz.

Decoder

In this VHDL-module, the value of the power orthe duty cycle, as selected by the user, is translated into

Fig. 8a. Register transfer level view of the FLGA controller.

Fig. 8b. Register transfer level view of the neuro-fuzzy controller.

Fig. 8c. Register transfer level view of the neural controller.

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7-segments format and adapted to be displayed on LCDdisplayer available on the development board.

ROM

The main aim of this VHDL-module is to store some

measured PV characteristics used to test the controllerin real-time.

Control block

The control VHDL-module is used to reset the systemwith a new value of duty cycle. The user can enter thedesired initial value of the duty cycle D acting on the input8 bits switch to start the search.

4.4. Simulation results using ModelSim and ISE

The ModelSim software is an HDL simulator manufac-tured by Mentor Graphics Corporation and can runindependently without ISE. The simulation results arebased on ModelSim Altera Starter version 6.5e (Altera,Inc.). Initially, the selected MPP-controller (i.e., the FLGA), starts to work with a low value of duty cycle, e.g.D= 10%, and subsequently the controller behaviour isobserved and then the required time to reach the maximumpower point is calculated automatically (in the screen ofModelSim software).The simulated output power and theduty cycle of the designed FLGA controller in the caseof rapid variation of solar irradiance are shown in

Fig. 9a. As can be seen, the designed controller reachesthe MPP very fast with negligible oscillation, which con-firms the accuracy of the developed code under VHDL aswell as the implementation of this controller.Fig. 9bshowsalso the floor planning of the FPGA implementing theMPPT controller and interface circuits. It is clearly

observed that the used Virtex 5 is largely sufficient to imple-ment the FLGA controller. With regard to the memoryspace (area) and power consumption required by eachintelligent controller, Table 8 reports the FPGA logicresources used to develop each controller. It is well knownthat power consumption is strongly dependent on the tar-get circuit including resource utilization, low-level featuressuch as logic partition, mapping, placement and route. Ascan be seen fromTable 8the consumption power for eachmodel is less than 1 W. X power analyzer available in ISE

tool is used to evaluate the power consumption. The totalpower consists of the sum of dynamic and static power;the dynamic is due to the component activity while staticpower represents the power consumed by the leakage cur-rent. ChipScope Pro Analyser of ISE could be also usedto analyse the designed controller.

Fig. 9a. The evolution of the PV power and duty cycle vs. time of the designed FLGA for rapid variation of solar irradiation and air temperature

(G= 700 W/m2, 24 C ! G= 300 W/m2, Ta= 19 C ! G= 700 W/m2, Ta= 24 C).

Fig. 9b. Floor planning of the FPGA implementing the MPPT (FLGA)controller and interface circuits (routing circuit).

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4.6. Test and verification

The co-simulation is a technique, which helps to evalu-ate how well a control algorithm operates in the device tar-get, FPGA chip. To test the developed MPPT controllersin real-time hardware co-simulation, two IVcharacteris-

tics measured in our laboratory for BP Solar 120 Wp mod-ule have been employed. Fig. 10a shows the laboratoryfacilities, it depicts also the measured IVcurves used here(Fig. 10b). A Virtex-5 ML501-XC5VLX50 development kit

(Xilinx, Inc.) has been employed to create an inexpensivehardware implementation for photovoltaic system applica-tions, it provides a complete solution for developing designand applications based on the XilinxVirtex-5 FPGA family(Xilinx, Inc.) (seeFig. 11a). Thus, the results obtained bydifferent MPPT-controllers as well as their performance

can be visualized in real-time.Fig. 11b shows the duty cyclegenerated by the designed FLGA controller, which is dis-played in PWM form with a scope under constant condi-tions, using real measured IV curve at G= 700W/m2

Table 8Device utilization summary and power consumption for each MPPT controller.

Device utilization summary Virtex-5(XC5VLX50)

Fuzzy logiccontroller

Neuralcontroller

ANFIScontroller

Optimal fuzzy logiccontroller

Available

Number of slice LUTs (look up tables) 3448 (11%) 5029 (17%) 457 (1%) 3773 (13%) 28,800Number of slice registers 164 (1%) 0 (0%) 107 (1%) 224 (1%) 28,800Number used as logic 3453 (11%) 3381(11%) 457 (1%) 3756 (13%) 28,800Number of bonded IOBs (input output

blocs)

18 (4%) 18 (4%) 18 (4%) 18 (4%) 440

Number of BUFCs (buffer clocks) 2 (6%) 2 (6%) 2 (6%) 4 (12%) 32Number of DSP48Es (digital signal

processor)2 (4%) 2 (4%) 47 (98%) 1 (2%) 48

Total dynamic power (W) 0.04146 0.00072 0.03738 0.04260 Total quiescent power (W) 0.42532 0.42459 0.42528 0.42538 Total estimated Power consumption (W) 0.46678 0.42531 0.46266 0.46798 Junction temperature (C) 52.1 51.9 52.1 52.1

0 2 4 6 8 10 12 14 16 18 200

1

2

3

4

5

6

Voltage (V)

Current(A)

BP Solar MSX 120W

G=700 W/mTc=24 C

(a)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Voltage (V)

Current(A)

BP Solar MSX 120W

G=300 W/mTc=19 C

(b)

Laboratoryfacilities

Jijel Univ.

UsedPVmoduleBPSolar

MSX120Wp

PV

Fig. 10. Laboratory facilities (a) measured IV characteristic (G= 700 W/m2, Ta= 24 C) and (b) measured IV characteristic (G= 300 W/m2,

T= 19 C).



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and T= 24 C. This is the final step, which confirms theimplementation of designed intelligent controllers intoFPGA chip. It should be noted that the employed Virtex5 is more powerful compared to Virtex 2 used in our con-trollers designed previously (Chekired et al., 2012); it is alsovery fast and has more memory space and facilities and lowpower consumption.

The implementation of such intelligent controllers intoFPGA for the tracking of the MPP is very promising andalso the VHDL barrier could now be resolved using anew package, called system generator created by Matlab/Simulink, which allows Xilinx chips to be programmedwith the common Matlab programming environment, Sim-ulink (Xilinx, Inc. 2008). System generator automaticallycompiles designs into low-level representations. It can also

make a co-simulation and rapid prototyping design. Exper-iments using hardware generation can suggest the hard-ware speeds that are possible and, through the resourceestimation, give a rough idea of the cost of the design inhardware. If a promising approach is identified, systemgenerator can create the bit stream (physical level) to theFPGA. It can also generate equivalent representations ofthe design, at the same or lower level, and furthermoreequivalent high-level module that performs a specific func-tion in applications external to system generator (Model-Sim hardware co-simulation) (Xilinx, Inc. 2008).

5. Comparative study

Table 9 presents a comparison of different intelligentMPPT controllers according to their complexity level, quickresponse (rapidity), efficiency, power consumption and

space (area) memory required in the FPGA. As shown inthe results presented above, the developed intelligent con-trollers provide accurate tracking of the MPP and improveconsiderably the efficiency of the PV system. With referenceto this table, the following key conclusions can be made:

It is clearly shown that the FL-optimized GA con-troller track the MPP very fast with insignificantfluctuation in the steady state even under a timevarying environment. Memory space requested isabout 45%. Thus, the method is more efficient andaccurate especially in rapid variation of weatherconditions. However, its implementation is relativelycomplex and needs high technical knowledge andskills in this area and it asks for a heavy program-

ming in VHDL language. Nevertheless, this problemcould be resolved using system generator.

The ANN-controller is relatively slow and less effi-cient compared to FLGA controller, its mainadvantage is that this technique is easy to be imple-mented and requires less memory space. However, itrequires the heuristic sense and it works as a blackbox. In addition, its robustness depends on the goodtraining parameters. Although, better performanceis observed in case of rapid variation of the weatherconditions, the main disadvantage of this techniqueis that it could fail when the PV modules start to bedegraded at approximately 10%, in this case trainingwith new data should be carried out periodically.Therefore, this is the most important point, whichis not cited in almost any published paper using anANN to track MPP.

Fig. 11. (a) The FPGA chip used for co-simulation, and (b) the duty cycleD generated by the FLGA controller, displayed in PWM form under constantconditions (G= 700 W/m2, Ta= 24 C) using real measured IVcurve.

Table 9Comparison of different intelligent MPPT controllers according to their complexity level, rapidity, efficiency and space memory required in the FPGA.

Controllers Complexity level Response t ime (Rapidity) Efficiency Oscillation around the MPPT Space memory (%)

FLGA Complex Very fast More efficient Negligible 45FL Simple Relatively fast Efficient Low 45ANN Medium Fast Efficient Low 25

ANFIS Medium Relatively fast Efficient Low 12

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The ANFIS controller exhibits good results com-pared to the ANN-controller for the point of viewof time response, efficiency and required space mem-ory. However, its robustness depends on the tuningof FL parameters and it is relatively easy to beimplemented compared to FLGA. It performs also

better in rapid variation of weather conditions. The fuzzy controller also can provide acceptableresults and require large memory space and easyimplementation compared to other techniques.However, the shortcoming of fuzzy computation isobtaining the correct fuzzy rules and membershipfunctions, which heavily rely on the prior knowledgeof the system and has an impact on the robustnessand reliability of the method. It could also be usedin the case of rapid variation of weather conditions.

Low power is consumed by the designed MPPT con-troller, which is less than 0.5 W. Space memory inthis Virtex 5 FPGA is largely sufficient to implement

our controllers.

6. Conclusions and perspectives

In this paper, four intelligent methods for tracking theMPP in photovoltaic systems have been designed in orderto improve the efficiency of PV systems under variableweather conditions (air temperature and solar irradiance).The effectiveness of these methods has been evaluated withdifferent simulation studies under Matlab/Simulink andModelSim. The advantages of the intelligent methods-

based MPPT controller are: they offer an alternativeapproach to conventional MPPT controllers; they exhibita faster converging speed, good performance, efficiency,less oscillation around the MPP under steady-state condi-tions, low power consumption, and no divergence fromthe MPP during varying weather conditions.

The implementation of these techniques has been dem-onstrated and co-simulated using FPGA chip Virtex5.The main disadvantage of these methods is that advancedtechnical knowledge and skills are required, especially inFPGA design and VHDL code. However, using Math-works Simulink and Xilinxs system generator, whichoffers a rapid prototyping implementation platform, rapidevaluation, is achieved.

It should be noted that for the case of partially shadingconditions, these methods should be modified or combinedwith other classical techniques. In the future, we will devoteour effort to the development of intelligent MPPT methodsoperating under partial showing and their possible imple-mentation into FPGA plat form in real time photovoltaicapplications.

Acknowledgments

The second author would like to thank the International

Centre for Theoretical Physics (ICTP), Trieste (Italy) for

providing the materials and the computer facilities for per-forming the present work. This work was also supported bythe TWAS under Grants (Ref. 09-108 RG/REN/AF/AC_C: UNESCO FR:3240231224, 12-194RG/REN/AF/AC_C: UNESCO FR:3240270869).

Appendix A

PV module specifications.

Designation BP Solar MSX120(W)Nominal power 120 WVoltage at MPP 17 VCurrent at MPP 7.06 AShort-circuit current 7.92 AOpen-circuit voltage 21.2 V

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