1_ANN&GA-MPPT-based Artificial Intelligence Techniques for Photovoltaic Systems

8/10/2019 1_ANN&GA-MPPT-based Artificial Intelligence Techniques for Photovoltaic Systems

1/21

Review

MPPT-based articial intelligence techniques for photovoltaic systemsand its implementation into eld programmable gate array chips:Review of current status and future perspectives

Adel Mellit a,b, Soteris A. Kalogirou c,*

a Renewable Energy Laboratory, Faculty of Sciences and Technology, Jijel University, 18000, Algeriab Unit de Dveloppement des quipements Solaires (UDES-EPST/CDER), Bousmail, Tipaza 42000, Algeriac Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603, Cyprus

a r t i c l e i n f o

Article history:Received 3 January 2014

Received in revised form

22 March 2014

Accepted 25 March 2014

Available online 28 April 2014

Keywords:Photovoltaic systems

Maximum power point tracking

Articial intelligence

Implementation

Programmable logic devices

Field programmable gate arrays chip

Real-time applications

a b s t r a c t

In this paper, the applications of articial intelligence-based methods for tracking the maximum power

point have been reviewed and analysed. The reviewed methods are based upon neural networks, fuzzylogic, evolutionary algorithms, which include genetic algorithms, particle swarm optimization, ant col-ony optimization, and other hybrid methods. Rapid advances in programmable logic devices (PLDs)

including eld programmable gate arrays (FPGAs) give good opportunities to integrate efciently such

techniques for real time applications. An attempt is made to highlight the future trends and challenges inthe development of embedded intelligent digital maximum power point tracking (MPPT) controllers intoFPGA chip. Special attention is also given to the cost, complexity of implementation, efciency, and

possible practical realization. We believe that this review provides valuable information for engineers,designers and scientist working in this area and show future trends in the development of embedded

intelligent techniques for renewable energy systems.2014 Elsevier Ltd. All rights reserved.

1. Introduction

Energy consumption in the word has increased noticeably dueto world population augmentation. The sustainable use of renew-able energy solves one of the major concerns of the world com-

munity, since the amount of fossil energy sources is no longersufcient. Among renewable sources, solar energy is one of themost promising nowadays[1]. Photovoltaic (PV) is expanding veryrapidly due to effective supporting policies and recent drastic cost

reductions [2]. PV is a commercially available and reliable tech-

nology with a signicant potential for long-term growth in nearlyall world regions. The IEA[2] estimates that by 2050, PV will pro-vide around 11% of global electricity production and avoid 2.3 Gt of

CO2 emissions per year. PV arrays have the advantage of directlyconverting light energy into electrical energy through semi-conductors [3]. Furthermore, they generate electricity from

sunlight instead of using fossil fuels, so no carbon dioxide is emittedin the process.

Tracking the maximum power point (MPP) of a photovoltaicmodule/array is an essential task in a PV control system, since itmaximizes the power output of the PV system, and therefore

maximizes the PV modules efciency. To enhance the conversionefciency of the electric power generation a maximum power pointtracking (MPPT) module (i.e., it consists of MPPT algorithm used tocontrol a DCeDC converter) is usuallyintegrated with the PV power

installations so that the photovoltaic arrays will be able to deliver

the maximum power available in real time under all possible sys-tem operating conditions.

In the last decade, several researchers have focused on various

MPP methods to track the maximum power of photovoltaic mod-ule/arrays[4e6]. These algorithms vary in many aspects, such assimplicity, required sensors, cost, range of effectiveness, conver-gence speed, correct tracking when irradiation and/or temperature

change, hardware needed for the implementation and popularity.Recently, Ishaque and Salam[7]reviewed classical techniques suchas incremental conductance and Hill Climbing (HC) and some otherrecent MPPT approaches using soft computing methods. Reisi et al.

[8] classied different MPPT methods based on three categories,

* Corresponding author.

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

Contents lists available atScienceDirect

Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m/ l o c a t e / e n e r g y

http://dx.doi.org/10.1016/j.energy.2014.03.102

0360-5442/

2014 Elsevier Ltd. All rights reserved.

Energy 70 (2014) 1e21
mailto:[email protected]:[email protected]://www.sciencedirect.com/science/journal/03605442http://www.elsevier.com/locate/energyhttp://dx.doi.org/10.1016/j.energy.2014.03.102http://dx.doi.org/10.1016/j.energy.2014.03.102http://dx.doi.org/10.1016/j.energy.2014.03.102http://dx.doi.org/10.1016/j.energy.2014.03.102http://dx.doi.org/10.1016/j.energy.2014.03.102http://dx.doi.org/10.1016/j.energy.2014.03.102http://www.elsevier.com/locate/energyhttp://www.sciencedirect.com/science/journal/03605442http://crossmark.crossref.org/dialog/?doi=10.1016/j.energy.2014.03.102&domain=pdfmailto:[email protected]:[email protected]


2/21

ofine, online and hybrid methods. Advantages and disadvantagesof each class were also compared based on simulations usingMATLAB/Simulink. MPPT-based soft-computing techniques havebeen recently reviewed by Salam et al. [9]. A detailed description

and classication of the MPPT techniques have been reported bySubudhi and Pradhan [10]. This classication is made based onfeatures, such as number of control variables involved, types of

control strategies employed, types of circuitry used suitability forPV system and practical/commercial applications. Bhatnagar andNema [11] have presented a comprehensive presentation ofworking principle of MPPT techniques, which were also comparedagainst each other in terms of some critical parameters. Another

comprehensive review can be also found in Ref.[12]. The capabilityof the application of articial intelligence techniques in modelling,prediction and control of photovoltaic systems has been demon-strated in an extensive review given by the authors in Ref. [13].

Additionally, the use of the intelligent techniques-based MPPtrackers should be noted. These are recently developed and used toimprove energy conversion efciency.

According to Tyugu[14]there are two different understandings

of articial intelligence (AI) as a researcheld: The rst approach

takes a philosophical attitude, and is interested in the possibilitiesof building articial systems with intelligent behaviour primarilyfor scientic purposes. The second approach is aimed at practical

applications, and is interested in building applications that possesssome intelligence, or more precisely, seem to be intelligent. Thisapproach became very popular with the development of expert

systems about thirty years ago. AI analysis is based on past historydata of a system (represented as a set of patterns) and it is thereforelikely to be better understood and appreciated by designers thanother theoretical or empirical methods. AI may be used to provide

innovative ways of solving design issues and will allow designers toget an almost instantaneous expert opinion on the effect of a pro-posed change in a design[15].

Advances in articial intelligent techniques embedded into eld

programmable gate arrays (FPGAs) platform include the application

of such technologies in real-time modelling, co-simulation andcontrol of photovoltaic systems[16e18].

FPGAs are reprogrammable silicon chips, which offer lower costimplementations since the functions of various components can be

integrated onto the same FPGA chip, and they can provide equiv-alent or higher performance with the customization potential of anApplication Specic Integrated Circuits (ASICs). They implement

circuits just like hardware, providing huge power, area, and per-formance benets over software, yet can be reprogrammed cheaplyand easily to implement a wide range of tasks. Just like computerhardware, FPGAs implement computations spatially, simulta-neously computing millions of operations in resources distributed

across a silicon chip. Such systems can be hundreds of times fasterthan microprocessor-based designs[19]. However, unlike in ASICs,these computations are programmed into the chip and are notpermanently frozen by the manufacturing process, which means

that an FPGA-based system can be programmed and reprog-rammed many times [19]. Furthermore, they are truly parallel innature, so different processing operations do not have to competefor the same resources. Each independent processing task is

assigned to a dedicated section of the chip, which can function

autonomously without any inuence from other logic blocks. As aresult, the performance of one part of the application is not affectedwhen you add more processing[20]. With reference to Cofer and

Harding[21]a constant of the FPGA industry has been a relentlesspace of innovation, enhancement and change. These technologyadvances have been targeted to provide the FPGA designer with

increased exibility and more design implementation options.Recent architecture advances include enhanced digital signal pro-cessing (DSP) support elements such as dedicated hardware mul-tipliers and larger blocks of embedded and distributed Random

Access Memory (RAM) with enhanced features, higher perfor-mance embedded processor cores, higher speed input/output (I/O)implementations and expanded FPGA conguration options. Theseadvances serve to expand the range of functionality FPGA compo-

nents can implement. FPGA tool set improvements have also

Nomenclature

TerminologyACO Ant Colony OptimizationANFIS Adaptive Neuro-Fuzzy Inference SystemANN Articial Neural NetworkASIC Application Specic Integrated Circuit

CLBs Congurable Logic BlocksD Duty cycleDSP Digital Signal ProcessorED Evolutionary Evolution

ENN Elmen Neural NetworkFCN Fuzzy Cognitive NetworksFL Fuzzy LogicFPGA Field programmable gate array

G Solar irradianceGA Genetic AlgorithmGKA Genetic k-Means AlgorithmGMPP Global Maximum Power Point

GMPPT Global maximum power point tracking

HCPV High Concentrator PhotovoltaicHDL Hardware Description LanguageHIS Hybrid Intelligent Systems

HNN Hopeld Neural NetworkIncCond Incremental Conductance

ISE Integrated Software EnvironmentMPP Maximum Power PointMPPT Maximum power point trackingNARMA Nonlinear Autoregressive Moving Average

OTP One-Time ProgrammableP&O Perturb and ObservePGS Power Generating SystemPID Proportional Integral Derivative

PSC Partial Shading ConditionsPSO Particle Swarm OptimizationPV PhotovoltaicPWM Pulse Width Modulator

RBFN Radial Basis Function NetworkSMC Sliding Mode ControlSTC Standard Test ConditionsSRAM Static Random Access Memory

VHSIC Very High Speed Integrated CircuitZSI Z-Source Inverter

SymbolsT Air temperature (C)

Vref Reference voltage (V)Iin Input current (A)Iout Output current (A)Vin Input voltage (V)Vout Output voltage (V)

A. Mellit, S.A. Kalogirou / Energy 70 (2014) 1e212


3/21

contributed signicantly to a design teams ability to take advan-tageof FPGA exibility and features. These broad enhancements are

requiring more FPGA designer cross training within the areas ofsystems, hardware, software, rmware and DSP engineering[21].

Conventional MPPT methods such as perturb and observe(P&O), variable step-size P&O and other improved versions of P&O

have been implemented into FPGA, due to their simplicity ofimplementation[22e25]. However, these algorithms are not ef-cient with respect to the MPPT-methods based on articial intelli-gence techniques (such as, fuzzy logic, neural network, etc.).

An attempt is made in this paper to present a complete detailedreview on the MPPT-based intelligent methods, as well as theirhardware implementation into FPGAs taking into account the ef-

ciency, cost, ease of implementation, stability, and robustness as

well as the response time of each controller. Challenges, opportu-nities and future research trends of implementing of intelligentMPPT methods into recongurable devices (FPGAs) are also pro-vided. Through this paper, we try also to answer the following key

questions:

- Is there a need to develop an effective MPPT method?

- Are AI techniques present a good solution for the development

of an effective (intelligent) MPPT controller?- Is there a need to implement an MPPT into FPGA chips?- Are FPGAs adequate for intelligent MPPT controllers?

- For which requirements may FPGAs be better than DSP andASIC? Why FPGA is chosen?

- Are the actual design tools of sufcient high-level for imple-menting intelligent MPPTs?

- Are advances of embedded intelligent MPPTs on FPGA easy fordesigners and developers?

This paper is organized as follows: a brief introduction to arti-

cial intelligence techniques is provided in the next Section. Shortdescription about programmable logic devices (PLDs) includingFPGA is provided in Section3. Section4outlines a state-of-the-art

overview on the applications of AI techniques in tracking the MPPunder uniform and non-uniform insolation conditions. Section 5highlights the implementation of AI techniques into FPGA chip.Concluding remarks including challenges and opportunities arereported in Section6.

2. Articial intelligence techniques

2.1. Denition of AI

John McCarthy, who coined the term Articial intelligence in1956 [26], denes it as the science and engineering of makingintelligent machines, especially intelligent computer programs.Denitions that are more recent speak of imitating intelligent hu-

man behaviour

, which is already a much stronger denition[27].Denitions of AI can be also classied into the following four

categories[28]:

- Systems that think like humans[29,30]- Systems that act like humans[31,32]

- Systems that think rationally[33,34]- Systems that act rationally[35,36].

In other word, AI is a term that in its broadest sense would

indicate the ability of a machine or artefact to perform the samekind of functions that characterize human thought. The term AI hasalso been applied to computer systems and programs capable ofperforming tasks more complex than straightforward program-

ming, although still far from the realm of actual thought. According

to Barr and Feigenbaum [37] AI is the part of computer scienceconcerned with the design of intelligent computer systems, i.e.,

systems that exhibit the characteristics associated with intelligencein human behaviourdunderstanding, language, learning,reasoning, solving problems and so on [38]. Several intelligentcomputing technologies are becoming useful as alternate ap-

proaches to conventional techniques or as components of inte-grated systems[39].

2.2. Branches of AI

AI consists of several branches, namely, expert systems (ES),problem solving and planning (PSP), knowledge representation(KP), common sense knowledge and reasoning (CSKR), logic pro-

gramming (LP), natural language processing (NLP), computer vision(CV), genetic programming (GP), non-monotonic reasoning (NMR),pattern recognition (PR), heuristics, robotics, hybrid intelligentsystems (HIS), epistemology, ontology, etc.

In this paper specic branches of AI are used like articial neuralnetworks (ANN), fuzzy logic (FL), evolutionary algorithms (EA) andvarious hybrids intelligent systems (HIS), which are combinationsof two or more of the branches mentioned previously [40e43]. A

brief introduction for each technique is given below.

2.2.1. Articial neural networksArticial neural networks are electrical analogues of the bio-

logical neural organs. Biological nerve cells, called neurons, receive

signals from neighbouring neurons or receptors through dendrites,process the received electrical pulses at the cell body and transmitsignals through a large and thick nerve ber, called an axon. In asimilar way, the electrical model of a typical biological neuron

consists of a linear activator, followed by a non-linear inhibitingfunction. The linear activation function yields the sum of theweighted input excitation, while the non-linear inhibiting functionattempts to capture the signal levels of the sum. The resulting

signal, produced by the electrical neuron, is thus bounded (ampli-

tude limited) [42]. Fig.1, shows a typical structure of a feed-forwardneural network.

Articial neural networks provide successful models and met-

aphors to improve our understanding of the human brain. Thefamiliar serial computer with its precise spatially allocated func-tions of memory, computation, control, and communications is apoor metaphor for a brain. Memory in a brain is distributed, with

your memory of, say a face, not precisely allocated to a small groupof neurons as they are on a workstation [44]. Furthermore, ANNs

Fig. 1. Feed-forward neural network[13].

A. Mellit, S.A. Kalogirou / Energy 70 (2014) 1e21 3


4/21

are viable computational models for a wide variety of problems,they play a very important role in prediction, approximation

functions modelling, control of complex systems forecasting,regression and other problems. Some applications in PV systemsare reviewed by Mellit and Kalogirou in Ref. [13].

2.2.2. Fuzzy logicThe logic of fuzzy sets was rst introduced by Zadeh[45], who

introduced the concept in systems theory and later extended it forapproximate reasoning in expert systems. Fuzzy logic deals withfuzzy sets and logical statements for modelling human-like

reasoning problems of the real world. A fuzzy set, unlike conven-tional sets, includes all elements of the universal set of the domainbut with varying membership values in the interval [0,1]. The mostcommon operators applied to fuzzy sets are AND(minimum), OR(maximum) and negation (complementation), where AND and

OR have binary arguments, while negation has unary argument[42]. It is based on fuzzy logic reasoning which employs linguisticrules in the form of IF-THEN statements. The principle of fuzzy logicis depicted inFig. 2.

Fuzzy logic control does not require a conventional model of theprocess, whereas most conventional control techniques (i.e.,

model-based control for example), require either an analyticalmodel or an experimental model [47]. The benet of the application

of fuzzy logic in control, complex system, engineering and realword problems has been well justied. Applications in photovoltaicare given in Ref.[13].

2.2.3. Evolutionary algorithmsIn articial intelligence, an evolutionary algorithm is a subset of

evolutionary computation, a generic population-based meta-heu-ristic optimization algorithm. An EA uses mechanisms inspired bybiological evolution, such as reproduction, mutation, recombina-tion, and selection. Candidate solutions to the optimization prob-

lem play the role of individuals in a population, and the tnessfunction determines the environment within which the solutions

live(as given by the cost function). Evolution of the populationthen takes place after the repeated application of the above oper-

ators[48]. Evolutionary computation is becoming common in thesolution of difcult, real world complex problems in engineeringand industry.

The main owchart of the main processes followed by the ma-

jority of evolutionary algorithms is given in Fig. 3.EA includes genetic algorithm (GA), particle swarm optimization

(PSO), differential evolution (DE), ant colony optimization (ACO),and others.

- Genetic algorithm

Genetic algorithm was introduced by John Holland[49]over the

course of the 1960s and 1970s, and nally popularized by one of his

students, David Goldberg, who was able to solve a difcult probleminvolving the control of gas-pipeline transmission for his disserta-tion[50]. Since then, many versions of evolutionary programminghave been tried with varying degrees of success. A GAis a stochastic

algorithm that mimics the natural process of biological evolution[41]. GAs are inspired by the way living organisms are adapted tothe harsh realities of life in a hostile world, i.e., by evolution andinheritance. The algorithm imitates in the process, the evolution of

a population by selecting only t individuals for reproduction.Therefore, a GA is an optimum search technique based on theconcepts of natural selection and survival of the ttest. It workswith a xed-size population of possible solutions of a problem,

called individuals, which are evolving in time. GAs nd extensiveapplications in intelligent search, machine learning and optimiza-

tion problems. Problem states in a GA are denoted by chromo-somes, which are usually represented by binary strings. A GA

utilizes three principal genetic operators; selection, crossover andmutation[42].

Some of the advantages of a GA are[51]:

Optimizes with continuous or discrete variables,

Do not require derivative information,

Simultaneously searches from a wide sampling of the costsurface,

Deals with a large number of variables,

Is well suited for parallel computers,

Optimizes variables with extremely complex cost surfaces (theycan jump out of a local minimum, etc.).

- Particle swarm optimization

Fig. 2. Fuzzy logic diagram (fuzzy inference system) [46].

Fig. 3. Evolution algorithm.



5/21

PSO is a stochastic, population-based EA search method,modelled after the behaviour of birdocks. The method was pro-

posed by Kennedy[52]. PSO is similar to GA in that it begins with arandom population matrix. Unlike the GA, PSO has no evolutionoperators such as crossover and mutation. The rows in the matrixare called particles (same as the GA chromosome). They contain the

variable values and are not binary encoded [48]. Nowadays, PSO is apromising method working in the direction of simulation andoptimization of difcult engineering and other nonlinear complexproblems.

- Differential evolution

The multi-agent heuristical optimization method known as

differential evolution (DE) is introduced initially by Storn and Price[53], and works by creating a new potential agent-position bycombining the positions of randomly chosen agents from its pop-ulation, and updating the agents current position in case of

improvement to its tness. Like GA, the DE method also employsoperators that are dubbed crossover and mutation (albeit withdifferent meanings), and which are typically applied in turn [54].

Differential evolution algorithm has been demonstrated to be an

efcient and effective optimization method[53].

- Ant colony optimization

Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to nd approximate solutions to difcultoptimization problems. In ACO algorithm initially introduced by

Dorigo[55,56], the inspiring source of ant colony optimization isthe foraging behaviour of real ant colonies, which is a probabilisticalgorithm aiming to nd the global optimal solution for a nonlinearcomplex problem. ACOmimicsthe foraging behaviour of the ants to

achieve optimization of the path in a graph.

2.2.4. Hybrid intelligent systems

Hybrid intelligent systems combine more than one of thetechnologies introduced above, either as part of an integratedmethod of problem solution, or to perform a particular task that isfollowed by a second technique, which performs some other task.For example, neuro-fuzzy is an efcient tool to deal with non-

linearly complicated systems, in which there are linguistic infor-mation and data information, simultaneously, i.e., to control aprocess, whereas in another hybrid intelligent system a neuralnetwork may be used to derive some parameters and a GA may be

used subsequently to nd an optimum solution to a problem[57,38].

3. Programmable logic devices

3.1. Denition

Programmable logic devices (PLDs) are divided into three pri-

mary architectural groups:

- Simple programmable logic devices (SPLDs)

- Complex programmable logic devices (CPLDs)- Field programmable gate arrays (FPGAs)

While each of these programmable logic device architectures

have typical focused applications, they also have some commonfeature overlap which leads to some overlap of applications.Fig. 4illustrates the overlap between the three PLD technologies. Forexample, some applications could be implemented in either a CPLD

or an FPGA.

3.2. Field programmable gate array

FPGAs are programmable semiconductor devices that are basedaround a matrix of congurable logic blocks (CLBs) connectedthrough programmable interconnections. As opposed to ASICs,where the device is custom built for the particular design, FPGAs

can be programmed to the desired application or functionality re-quirements[58].

The fundamental FPGA structures are as follows [59]: logic

blocks, routing matrix & global signals, I/O blocks (IOBs), clock re-sources, multiplier, memory and advanced features. Generally allXilinx FPGAs contain the same basic resources as shown inFig. 5[60].

Although one-time programmable (OTP) FPGAs are available,

the dominant types are SRAM-based which can be reprogrammedas the design evolves. The logic and routing elements in an FPGAare controlled by programming points, which may be based onantifuse, Flash, or SRAM technology. For recongurable computing,

SRAM-based FPGAs are the preferred option, and in fact are theprimary style of FPGA devices in the electronics industry as a whole.In these devices, every routing choice and every logic function arecontrolled by a simple memory bit. With all of its memory bits

programmed, by way of a conguration le or bit stream, an FPGAcan be congured to implement the users desired function. Thus,the conguration can be carried out quickly and without perma-

nent fabrication steps, allowing customization at the users elec-tronics bench, or even in the nal end product. This is why FPGAsare eld programmable, and why they differ from mask-programmable devices, which have their functionality xed by

masks during fabrication[19].FPGAs allow designers to change their designs very late in the

design cyclee even after the end product has been manufacturedand deployed in the eld. In addition, Xilinx FPGAs allow for eld

upgrades to be completed remotely, eliminating the costs associ-ated with re-design or manually updating electronic systems [58].ASIC and FPGAs have different value to designers and they must becarefully evaluated before choosing any one over the other. While

FPGAs used to be selected for lower speed/complexity/volume

Fig. 4. PLD categories[21].

Fig. 5. Example of distribution of CLBs, IOBs, PIs, RAM blocks, and multipliers in Virtex

II[60].



6/21

designs in the past, todays FPGAs easily push the 500 MHz per-formance barrier. With unprecedented logic density increase and a

host of other features, such as embedded processors, DSP blocks,clocking, and high-speed serial at ever-lower price points, FPGAsare a compelling proposition for almost any type of design[58].

FPGAs based hardware solutions, using the device s inherent

parallelism, have been recently received increased attention, asthey allow engineers/designers to develop efcient hardware ar-chitectures based on exible software. Additional FPGA advantagesinclude the fact that their hardware logic is extremely fast, much

faster than software-based logic. They are easier to interface to theoutside world, either through custom peripherals or via glue logicto custom co-processors. They are also better suited for bit-leveloperations than a microprocessor. Other advantages are: ability to

control part obsolescence through design ownership and viabletechnology roadmap, improved design update and enhancementoptions, higher system performance, lower tool costs and veri-cation costs than ASIC implementation, lower implementation

costs, and allows consolidation of multiple components into asingle component. More potential FPGA advantages are listed inRef. [21]. Furthermore, FPGAs are especially suited to control ap-

plications requiring custom hardware, such as digital communi-

cation protocols, rapid control prototyping, hardware-in-the-loopsimulation, in-vehicle data acquisition, machine control and ma-chine condition monitoring[61]. As reported in Ref.[62]ASIC and

FPGA developments can be done in parallel to get the benets ofboth technologies. The FPGA devices are used as prototypes and ininitial shipments to cut the manufacturing lead-time. When theASIC devices become available later, they are used for volume

production to reduce cost. Actually, modern FPGAs embed dedi-cated multipliers to increase the speed of multiply-accumulateoperations that are essential for many DSP designs. However, thebest system performance relies on more than raw multiplier speed

[63]. It is critical to couple these multipliers with a complementary

logic structure and routing fabrics of the same performance. Forexample, The Stratix II family seamlessly integrates DSP blocks that

operate at up to 450 MHz with high performance adaptive logicmodules (ALMs) and routing fabric to offer the highest systemperformance for the DSP designs [63]. A typical FPGA mapping owis shown inFig. 6.

3.3. Hardware description language (VHDL)

Generally, implementation of any algorithm into FPGA can be

done by using a hardware description language, such as VHDL.VHDL is a hardware description language; it describes the behav-iour of an electronic circuit or system, from which the physicalcircuit or system can then be implemented. VHDL stands for VHSIC

Hardware Description Language. VHSIC is itself an abbreviation forVery High Speed Integrated Circuits, an initiative funded by theUnited States Department of Defence in the 1980s that led to the

creation of VHDL[64]. Itsrst version VHDL 87 was later upgradedto the so-called VHDL 93. VHDL was the original and rst hardwaredescription language to be standardized by the Institute of Elec-trical and Electronics Engineers, though the IEEE 1076 standard. An

additional standard, the IEEE 1164, was later added to introduce a

multi-valued logic system.As reported in Ref. [64] a fundamental motivation to use VHDL is

that VHDL is a standard. The two main immediate applications of

VHDL are in the FPGA and in the eld of ASICs. Once the VHDL codeis written, it can be used either to implement the circuit in a pro-grammable device (from Altera, Xilinx, Atmel, etc.) or can be sub-mitted to foundry for fabrication of an ASIC chip. Currently many

complex commercial chips (for example, microcontrollers) aredesigned using such an approach.

Sour

L

Tec

Plac

Tec

01001001

ce code (VHD

Verilog)

ogic synthesis

nology mappi

ment & Routi

nology mappi

Bitstream

11011

FPGA

or

g

g

g

Fig. 6. A typical FPGA mapping

ow[19].

Synthesis

V

(Regist

(

Opt

(

Ph

VHDL entry

ter Transfer lev

Netlist

(Gatelevel)

timizedNetlist

(Gatelevel)

O

ptimisation

Compilation

Placeand

route

hysicaldevice

vel)

Simulation

Simulation

Fig. 7. VHDL design

ow[64].



7/21

A digital system can be described at different levels of abstrac-tion and from different points of view. An HDL should faithfully and

accurately model and describe a circuit, whether already built orunder development, from either the structural or behaviouralviews, at the desired level of abstraction. Because HDLs aremodelled after hardware, their semantics and use are very different

from those of traditional programming languages [62]. Figs. 7 and 8depict a summary of VHDL design ow. Implanting a VHDL code isprincipally a two-step process, i.e., synthesis and placement androuting described bellow[65].

3.3.1. SynthesisSynthesis involves compiling the VHDL code with tools (e.g.

Xilinx Foundation ISE 11.1i) which is a commercially available tool.The result of this compilation is a ip-op and logic function

transcription of the high-level functionalities. Some functions canbe resolved in different ways, depending on the target component.VHDL codes can be simulated using ModelSim Xilinx or other tools.

3.3.2. Placement-and-routingThe result of the placement-and-routingis the nal code to be

implanted on the FPGA. An auxiliary result is the VHDLle givingthe operation of the implanted code and taking the propagationtime of the target device into account. This le can be used in co-simulation and this result in a representation of a virtual proto-

type. This allows checking that the placement-and-routing hasnot altered the performance and that the synchronization of allsignals is compatible with the propagation times.

4. Application of articial intelligent based method for MPP

tracking

As reported in Table 1[2], the current efciencies of different PVtechnology commercial modules varied between 6 and 20%. In

addition, the efciency of a photovoltaic cell/module is quitedependent to the environmental conditions, such as solar irradi-

ance, air temperature, dust accumulation, shading, mismatch, andother less important parameters. With reference to Table 1, the lowenergy conversion efciency of PV materials remains a barrier andnecessitates tracking the maximum power point of the PV moduleto ensure the maximum energy production.

Extracting the optimal power from a PV arrays has beenaddressed generally in two ways:

- Optimal conguration of PV arrays.- Design efcient MPPT algorithms.

In this paper, we focuson the second category of methods. As anexample, Fig. 8 shows the power-voltage characteristics underuniform and non-uniform insolation conditions.

As can be observed, under uniform insolation conditions (solid

line), thePeVcharacteristic presents a unique point, so-called theMPP, in which the PV array operates with maximum efciency andproduces maximum output power. Under non-uniform insolationconditions (dotted line), it is possible to have in the PeVcharac-teristic multiple local MPP and only one global MPP for the entirearrays. The non-uniform insolation occurs quite frequently due toclouds, trees, electric poles, and the shadow of neighbouringbuildings. In such a situation, the produced power by photovoltaic

modules/arrays can be reduced signicantly, and therefore devel-opment of an efcient technique to keep the system working in itsglobal MPP continuously is a big challenge.

The efcient operation of photovoltaic systems requires an op-

timum transfer of energy generated in the array to load. For this, anadaptive DCeDC converter is usually added to photovoltaic sys-tems; however, to keep a photovoltaic module/array working in itsMPP, an efcient MPPT algorithm should be integrated, see for

exampleFig. 9.An overview on the applications of AI techniques-based MPPT

for photovoltaic systems is provided in this section.Table 2reportsa summary of different efcient MPPT methods using intelligentMPPT controllers (such as fuzzy logic, neural network, evolutionary

algorithms, and hybrid systems).As can be observed the area of application includes both stand-

alone and grid-connected PV systems under uniform insolation andpartial shading conditions.

4.1. Application of FL for MPP tracking

Fuzzy Logic Control (FLC) belongs to the class of articial intel-ligent control. FLC utilizes knowledge-based decision-making

employing techniques of fuzzy logic in determining the controlactions. The application of fuzzy logic to track the MPP in photo-voltaic systems has beenwidely used with good efciency [66e82].Table 3summarizes the applications of fuzzy logic controller for

tracking the MPPT of photovoltaic systems whereas Fig. 10showsthe basic schematic block diagram of two-inputs one-output digitalfuzzy logic controller.

The application of fuzzy logic for tracking the MPP in photo-

voltaic systems has been rstly introduced in Refs. [66e69].

Power

Voltage

Uniform conditionsNon-Uniform conditions

Global MPP

Local MPPs

*

*

Fig. 8. PeVcharacteristics of a photovoltaic array under uniform (solid line) and non-

uniform (dotted line) insolation conditions.

Table 1

Current efciencies of different PV technology commercial modules[2].

Wafer-based c-Si Thin lms

SceSi mc-Si a-Si, a-Si/mc-Si CdTe CIS/CIGS

14e20% 13e15% 6e9% 9e11% 10e12%

Fig. 9. Example of a stand-alone PV system with integrated MPPT algorithm.



8/21

Patcharaprakiti et al.[70]have proposed a method of MPPT using

adaptive fuzzy logic (AFL) control for grid-connected PV systems.The system is composed of a boost converter and a single-phaseinverter connected to a utility grid. Results show that this systemis able to adapt the fuzzy parameters for fast response, and good

transient performance, which is insensitive to variationsin external

disturbances. The efciency of the system depends on the insola-tionvariation. A novel and simple on-line FL-based dynamic search,detection and tracking controller developed to ensure MPP opera-

tion under variations in solar insolation, ambient temperature andelectric load is proposed by Atlas and Sharaf[71]. According to theauthors, MPP-search and detection algorithm is fully dynamic in

nature and operates without requiring direct measurement orforecasted PV array information about the irradiation and tem-perature. The method is also tested under rapid variation of irra-diance and simulation results conrm the effectiveness of the

method.Lian et al. [72] have proposed a TakagieSugeno (TeS) fuzzy

method to deal with the power-tracking problem of the powergenerating systems. The designed method has been veried

experimentally using dSPACE. Gounden et al. [73] presented an

MPPT controller for a grid-connected PV generation system, whichis a representative example of this category and proved that the

fuzzy logic control is an effective tool to extract maximum power tothe grid. A microcontroller has been programmed to generateringpulses to the thyristors in the inverter. The method takes also inconsideration the shading problem of PV panel. According to the

authors, when a fraction of a module is shaded, the panel voltageand the panel current get reduced resulting in decrease ofmaximum power from the array. Hence, the ring angle corre-spondingly gets reduced from the value when no shading occurs.

TakagieSugeno (TeS) fuzzy-model-based approach is designed totrack the MPPT [74]. The proposed TeS fuzzy controller directlydrives the system to the maximum power point without searchingthe maximum power and measuring insolation. According to the

author, when considering disturbance and uncertainty, robustMPPT is guaranteed by advanced gain design. The developedcontroller is realized by a DSP-based control card (dSPACEDS1104).The proposed controller has a strict stability and performance

analysis, and it could be implemented easily.Chiu and Ouyang [75] proposed a unied TeS fuzzy MPPT

control method for uncertain solar power generation systems. Ac-

cording to the authors, advantages of the proposed control method

are summarized as:

1) no coordinate transformation and no calculation of the

maximum power operational point is required;2) the overall stability has strict analysis, which is lacked in

traditional methods; and3) better control performance is obtained in comparison with the

traditional methods from theoretical analysis and experiments.

Table 2

Summary of the numbers of applications presented in tracking the MPP for

photovoltaic systems using articial intelligence techniques.

AI-techniques Area of applications Number of

applications

Fuzzy logic

controllers

- MPP tracking in

PV modules

- Grid-connected

PV systems underuniform and

non-uniform insolation- Water pumping

photovoltaic systems

- Stand-alone


17

Neural

networks

- MPP tracking in

PV module- Grid-connected

PV systems

- High concentrationPV under uniform

insolation

- Stand-alone


15

Neural networksand fuzzy logic

controller

- MPP tracking inPV systems under

uniform and non-uniform

insolation conditions

13

Genetic algorithm

and fuzzy logic

controller

- Stand-alone


- Photovoltaic module

5

Genetic algorithmand neural networks

controller

- Stand-alonephotovoltaic systems

- Photovoltaic module

7

Evolutionary

algorithms

- Tracking the global MPP

under partial shading

conditions of PV systems

- Partial shading

photovoltaic generator

- Stand-alone PV system

with inductionmotor drive

13

Application of

hybrid methods

- Tracking the global

MPP under partial

shading conditions

of PV systems

10

Table 3

Summary of the applications of fuzzylogic for MPP trackingof photovoltaic systems.

# Authors Reference Year Subject

1 Won et al. [66] 1994 MPPT forPV arrays

2 Senjyu and

Uezato

[67] 1994 MPPT for

PV arrays

3 Simoes e t al. [68] 1998 Photovoltaic

system4 Mahmoud et al. [69] 2000 Photovoltaic

system

5 Patcharaprakiti et al. [70] 2005 Grid-connectedphotovoltaic system

6 Altas and Sharaf [71] 2008 Photovoltaic solar

energy systems/rapid

variation of insolation

7 Lian et al. [72] 2008 Micro-grid

photovoltaic system

8 Gounde n et al . [73] 2009 Grid-connected


9 Chiu [74] 2010 Solar power

generation systems10 Chiu and Ouyang [75] 2011 Control of uncertain


11 Messai et al. [76] 2011 Photovoltaic module

12 Algazar et al. [77] 2012 Stand-alone water

pumping system

13 Ramaprabha et al. [78] 2012 Partially shaded solar

photovoltaic system

14 Al Nabulsi and

Dhaouadi

[79] 2012 Stand-alone PV system

15 Chao et al . [80] 2012 Generic photovoltaicsystem for two-stage

DCeDC converter

16 Alajmi et al. [81] 2013 Partially shadedphotovoltaic systems

in microgrids

17 Dounis et al. [82] 2013 Photovoltaic systems



9/21

Messai et al. [76] have designed a fuzzy-logic controller (FLC) forseeking the MPP deliverable by a photovoltaic module using themeasured values of the photovoltaic current and voltage. Thesimulation results obtained show a satisfactory performance with a

good agreement between the expected and the obtained values. Asimilar FLC was developed in Ref. [77]to track the MPP of a stand-alone PV water pumping system. The developed intelligent method

was tested under variable temperature and insolation conditions. Itwas shown that the system with MPPT using FLC increase the ef-

ciency of energy production from the PV.A modied Fibonacci search based MPPT scheme and FLC for a

PV array is introduced in Ref.[78]. Results show that the method

tracks very fast the global MPPT. In addition, the GA optimizedfuzzy control improved the tracking performance compared withthe conventional PI controller and FLC which avoids the tuning of

controller parameters. A new digital control scheme for a stand-alone PV using FL and dual MPPT controller is proposed in Ref.[79]. Two controllers were designed, therstone is an astronomicaltwo-axis sun tracker that keeps maximum radiation on the panel

throughout the day, and the second one is a new fuzzy-based MPPTtechnique to change adaptively the P&O perturbation step size. The

designed system has been developed and implemented on DSP(TMS320F28335eZdsp). The results show that the proposed control

scheme achieves stable operation in the entire region of the PV

panel and therefore eliminates the resulting oscillations around theMPPT.

Chaoet al. [80] developed an intelligent MPPT of PV systembased on FLC. It has been shown that, the optimized tracking speed

of the proposed FC-MPPT is in fact more stable and faster than thegeneral P&O method with the boost voltage capable of offering astable DC output. The method was veried experimentally using a

DSP (TMS320F2812), and the efciency is about 96%. Recently, amodied fuzzy-logic-based MPPT has been proposed to extract theGMPP (Global Maximum Power Point) under partially shaded PVsystem conditions[81].

An advanced microcontroller (Inneon TriCore/TC1796)is used

to realize the proposed MPPT. The controller offers accurateconvergence to the global maximum operating point underdifferent PSCs. The results of the proposed MPPT exhibit a faster

converging speed, less oscillations around the MPP under steady-state conditions, and no divergence from the MPP during varyingweather conditions. Dounis et al.[82]proposed a methodology totrack the MPP using feedback control based on a proportionaleintegral-derivative (PID) controller tuned by fuzzy gain schedulingand online adaptation of the scaling factors. Results showed that

the designed adaptive approach achieve a good maximum poweroperation under any conditions such as different levels of solar

radiation and PV cell temperature for varying PV sources.

4.2. Application of ANNs for MPP tracking

MPPT-based ANN controller has been largely used in photo-voltaic systems. The capability of the ANN to track the MPPT underuniform atmospheric conditions has been proven [83e97].Table 4

reports different MPPT methods based on neural networks forphotovoltaic systems. Fig. 11 depicts an example of a schematicdiagram of MPPT-based ANN controller. In this method,Vrefor MPPcould be estimated from measured PV voltage and current or

measurement of solar irradiance and air temperature, however, an

additional controller is required, e.g. PID or other.Hiyama et al. [83] and Hiyama and Kitabayashi [84] used a

neural network for estimating the maximum-power generation

from PV module using environmental information. The proposednetwork can be utilized for the prediction of the next days gen-eration from the PV systems by using the predicted information

from a weather ofce. According to the authors, the proposedmethod gives more accurate prediction compared to the predictionobtained by using the conventional multiple regression models.Veerachary and Yadaiah[85]presented an application of an ANN

for the identication of the optimal operating point of a PV sup-plying a separately excited DC motor, driving two different loadtorques. They found that the ANN provides a highly accurateidentication/tracking of optimal operating points even with sto-

chastically varying solar insolation. Al-Moudi and Zhang[86]and

Fig. 10. Example of a basic MPPT-based fuzzy logic controller.

Table 4

Summary of applications of ANN techniques for MPP tracking in PV systems.


1 Hiyama et al. [83] 1995 Photovoltaic modules

2 Hiyama and Kitabayashi [84] 1997

5 Veerachary and Yadaiah [85] 2000 Photovoltaic system

supplied DC motors

3 Al-Moudi and Zhang [86] 2000 Grid-connected


4 Z hang et al . [87] 2002 Grid-connected


6 Ocran et al . [88] 2005 Solar electric vehicle7 Bahgat et al. [89] 2005 Photovoltaic systems

8 Nguyen et al. [90] 2007 Photovoltaic arrays

9 Lee et al. [91] 2010 Photovoltaic systems

10 Rai et al. [92] 2011 Solar photovoltaic

systems

11 Islam and Kabir [93] 2011 Photovoltaic systems

12 Kassem [94] 2012 Photovoltaic generator

powered DC

motor-pump system

13 Liu et al. [95] 2013 Photovoltaic systemsoperating under fast

changing environments

14 Almonacid et al. [96] 2013 High ConcentratorPhotovoltaic (HCPV)

module

15 Ben Ammar et al. [97] 2013 PV/T pumping system



10/21

Zhang et al.[87]presented an application of radial basis functionnetworks (RBFNs) for solar array modelling and maximum-power

point prediction. The proposed RBFN model can lead to energysaving, and it can calculate MPPs accurately and without searchingaround the optimal power point, which is required by the P&Omethod. The RBFN-based scheme can be used to predict the

reference voltage on-line for the power conditioner of any PV-grid-connected system.

Ocran et al. [88]used an ANN MPPT for solar electric vehicles.The MPPT is based on a highly efcient boost converter with

insulated gate bipolartransistor (IGBT) power switch. The referencevoltage for the MPPT is obtained by an ANN with gradient descentmomentum algorithm. The experimental and simulation resultsshow that the proposed scheme is highly efcient. Bahgat et al. [89]

presented an algorithm for maximum power point trackingcontroller for PV systems using neural networks. According to theauthors, the experimental results showed that the PV system withMPPT always tracks the peak power point of the PV module under

various operating conditions. The MPPT transmits about 97% of theactual maximum-power generated by the PV module. The MPPTnot only increases the power from the PV module to the load, butalso maintains longer operating periods for the PV system. The air

velocity and the air mass ow rate of the mechanical load areincreased considerably, due to the increase of the PV system power.It is also found that the increase in the output energy dueto the useof MPPT is about 45.2% for a clear sunny day. A neural network

based model of the shadow effect on maximum output power ofthe solar PV array is described by Nguyen et al.[90]. It is assumedthat shading due to moving objects such as clouds has a uniform

effect on the PV array, and hence, it uses only the sun position toconsider the effect of partial shading. This is advantageous as it

needs fewer inputs to train the neural network. However, theassumption of a uniform shading effect due to mobile objects is notalways valid.

An articial neural network based MPPT method has been

proposed for searching maximum power point [91]. The methodcombines an ANN-based model and the incremental conductance

method. The proposed algorithm is veried experimentally usingdSPACE 1104 with good accuracy. Rai et al.[92]proposed an ANN

based maximum power point tracking controller for Solar PV sys-tem and analysed it in MATLAB/Simulink. The model was used topredict maximum power voltage and maximum power currentunder variable atmospheric and load conditions. According to the

authors, the simulated results show that the ANN based controllerin its MPPT performance excels over the conventional PIDcontrollerand avoids the tuning of controller parameters. Islam andKabir[93]used a neural network to track the MPP of PV systems

and the MATLAB/Simulink is used to simulate the developed ANN-based MPPT algorithm. According to the authors the ANN-basedalgorithm performs better than fuzzy logic with changes of solarirradiance and air temperature. An intelligent (NARMA-ANN)

method for tracking the MPP in a photovoltaic water pumping wasproposed in Ref.[94]. Simulation results show that accurate MPPTtracking performance of the proposed system has been achieved.Further, the performance of the proposed ANN controller is

compared with a PID controller through simulation studies. Theobtained results demonstrate the effectiveness and superiority ofthe proposed approach.

A new embedded digital MPPT system based on ANN is recently

developed by Liu et al.[95]. The advantages of the proposed systeminclude low computation requirement, fast tracking speed and highstatic/dynamic tracking efciencies. In addition, using the devel-oped neural network model, the photovoltaic generation systems

user can apply the developed MPPT controller to any photovoltaicmodule without the need to modify the rmware of the photo-

voltaic generation system. An ANN-based model is developed byAlmonacid et al.[96]to predict the maximum power of an HCPVmodule using easily measurable atmospheric parameters. The re-

sults showed that using atmospheric parameters, the proposedANN is capable of estimating the maximum power of an HCPVmodule with a root mean square error of 3.29%. Ben Ammar et al.

[97] have suggested a PV/T control algorithm based on ANN todetect the optimal power operating point (OPOP) by consideringthe PV/T model behaviour. Simulation results conrm that the

approach delivers fast and accurate PV/T ow rate control.

4.3. Application of combined ANNs and FL for MPP tracking

Articial neural networks and fuzzy logic have been combined

in order to improve the ef

ciency of the tracking MPP controller;

Fig. 11. Example of MPPT-based ANN controller.

Table 5

Summary of applications ANN-FL techniques for MPP tracking in PV systems.


1 Veerachary et al. [98] 2003 Coupled-inductor

interleaved-boost

converter suppliedPV systems

2 Khaehintung et al. [99] 2003 Photovoltaic systems

3 Kottas et al. [100] 2006 Photovoltaic array

4 Karlis et al. [101] 2007 Photovoltaic systems

5 Syafaruddin et al. [102,103] 2009 Photovoltaic systems


6 Iqbal et al. [104] 2010 Photovoltaic module

7 Aymen et al. [105] 2010 Three phase

grid-connected

Photovoltaic system8 Ben Salah and Ouali [106] 2011 Photovoltaic systems

9 Subiy anto e t al . [107] 2012 Photovoltaic systems

10 Abu-Rub et al. [108] 2012 Photovoltaicgeneration systems

11 Afghoul et al. [109] 2003 Photovoltaic systems

12 Tar ek et al. [110] 2013 P V mo dule



11/21

here this technique is applied for photovoltaic systems under uni-form and non-uniform conditions. The applications of hybrid sys-

tems, whichcombine ANNs and FL for MPP tracking, are reported inTable 5. Fig. 12 shows a block diagram of MPPT-based ANFIScontroller. This diagram is similar to Fig. 5 just FL is replaced byANFIS controller.

Veerachary et al. [98] proposed a feed-forward MP-pointtracking scheme for the coupled-inductor interleaved-boostconverter-fed PV system using a fuzzy controller. Simulation andexperimental results demonstrated the peak power tracking

capability of the proposed scheme. It was also demonstrated thatthe fuzzy control improves the tracking performance compared tothe conventional PI controller and, thus, avoids the tuning ofcontroller parameters. Khaehintung et al. [99], proposed an adap-

tiveANFIS to track the MPP in PV systems inwhich the sliding modecontrol (SMC) was used to eliminate the need for training of theANFIS. Simulation and experimental results conrm the ability ofthe technique to track the MPP with good accuracy. Kottas et al.

[100] developed a method for MPPT using a fuzzy cognitivenetwork. According to the authors, the methodology can be appliedon any photovoltaic array of the market. Due to the existence of the

fuzzy cognitive networks (FCN) the method could track and adapt

to any physical variations of the photovoltaic array through time.Therefore, the method is guaranteed to present its very good per-formance independently of these variations. A novel MPPT method

based on FCN is proposed by Karlis et al. [101]which give a goodmaximum-power operation of any PV array under different con-ditions such as changing insolation and temperature. The numeri-cal results show the effectiveness of the proposed algorithm.

According to the author, the total annual error for the year 2002 ofthe P&O method is estimated to be 6.61%.

Syafaruddin et al. [102,103]proposed a novel MPPT algorithmusing articial neural network and fuzzy logic with polar infor-

mation controller. The ANN with three-layer feed-forward istrained once for several partial shading conditions (PSC) to deter-mine the Global MPP (GMPP) voltage; therefore, it is system

dependent. Moreover, this method uses insolation and temperatureas inputs to obtain GMPP, while this information is often notavailable in power generation system. An ANFIS based MPPTcontroller was developed by Iqbal et al. [104]. The ANFIS is trainedto estimate the MPP under various solar irradiance and air tem-

perature. Results indicate that the proposed MPPT effectively ex-tracts maximum available power from a solar PV module. Aymenet al. [105] proposed a novel methodology for maximum powerpoint tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV)

system using neuro-fuzzy network. Simulation results underseveral rapid irradiance variations showed that the MPPT methodfullled the highest efciency compared to a conventional singleneural network and the Perturb and Observe (P&O) algorithm

dispositive. Ben Salah and Ouali [106] carried out a comparative

studybetween the fuzzy logic and the NN to track the MPP forsolarPV system. The developed MPPT controllers receive solar radiationand photovoltaic cell temperature as inputs, and estimate the op-timum duty cycle corresponding to maximum power as output.

According to the authors, the FL controller cangenerate up to 99% ofthe actual maximum power and the NN controller can generate upto 92% of it. Subiyanto et al. [107]developed an intelligent methodto track the MPP of PV system by using a Hopeld neural network

(HNN) optimized fuzzy logic controller. According to the authors,

the proposed HNN optimized FLC can provide accurate tracking ofthe PV maximum power point and improve the efciency of PVsystems. An articial-intelligence-based solution to interface and

deliver maximum power from a photovoltaic (PV) power gener-ating system in standalone operation is proposed by Abu-Rub et al.[108]. The closed-loop control of the Quasi Z-Source Inverter (QZSI)

regulates the shoot through duty ratio and the modulation index toeffectively control the injected power and maintain the stringentvoltage, current, and frequency conditions. Other methods arerecently published in Refs.[109,110].

4.4. Application of combined FL and ANN with GA for MPP tracking

Applications of combined FL, ANN and GA to track the MPP are

summarized in Table 6. In these applications, genetic algorithms areused generally to tune the fuzzy logic parameters, as well as tooptimize the structure of the neural network (i.e., the number ofhidden layers, and neurons) in order to improve the efciency ofthe controller.

Akkaya et al. [111] presented a brushless DC motor drive forheating, ventilating and air conditioning fans, which is the load of aPV system with an MPPT. The MPPT controller is based on a geneticassisted multilayer perceptron neural network (GA-MLP-NN)

structure and includes a DCeDC boost converter. The DSP-basedunit provides rapid achievement of the MPPT and current controlof a brushless DC (BLDC) motor drive. The performance results ofthe system are given and experimental results are presented for a

120 W laboratory prototype. Larbes et al.[112]developed an MPPT-

Fig. 12. Example of a block diagram of MPPT-based ANFIS controller.

Table 6

Summary of applications of combined ANN and FL with GA techniques for MPP

tracking in PV systems.


1 Akkaya et al. [111] 2007 Photovoltaic system

with A brushless DC

(BLDC) motor drive

2 Larbes et al. [112] 2009 Photovoltaic systems

3 Liao [113] 2010 Photovoltaic MPP prediction4 Messai et al. [46] 2011 Photovoltaic systems

5 Afsin and Akkaya [114] 2012 Sand-alone PV systemwith induction motor drive



12/21

based FL controller, which is optimized by using a GA in order to

improve the efciency of the system under variable temperatureand irradiance conditions. The results show that the developed FLcontroller with GA performs better than the classical fuzzy logiccontroller. A hybrid Genetic k-Means Algorithm (GKA) was pro-

posed by Liao to improve the effectiveness of maximum powerpoint track[113]. By precisely determining the number of centersand the clustering of the training patterns using the proposedmethods, the objective of accurately and rapidly approximate the

MPP of PV systems has been achieved with the least squares cri-terion in RBFN. Messai et al.[46]demonstrated that the applicationof genetic algorithms to fuzzy logic controllers (GA-FL) design holdsa great deal of promise in overcoming two of the major problems in

fuzzy controller design; design time and design optimization. It wasconcluded that the performance of the new GA-FL based MPPT isbetter than the one obtained with classical FL and P&O controllers.

Afsin and Akkaya[114]proposed an intelligent GA-ANN basedMPPT algorithm for a stand-alone PV system with direct-coupledinduction motor drive. In this method, a PI controller is adoptedfor simple implementation. The developed algorithm was imple-

mented on DSP. Experimental results demonstrate the effectivenessof the proposed method.

4.5. Application of evolutionary algorithms for MPP tracking

Table 7depicts the applications of evolutionary algorithms suchas GA, PSO and ACO for tracking the MPP in photovoltaic systems.

Particle swarm optimization and ant colony optimization

algorithms become a successful alternative for the conventionaltuning method to adapt controllers. Recently, these techniques

have justied their capability to track the global MPP for photo-voltaic systems. They are employed to track the MPP under uniformand non-uniform conditions with successful results. As an example,Fig. 13shows a owchart of a standard PSO used in tracking of MPP.

Chen et al.[116]presented an MPPT method based on biologicalswarm chasing behaviour to increase the MPPT performance. Thismethod is only applicable when the entiremodule is under uniforminsolation conditions, hence PSC is not considered. Taheri et al.

[117] have used a differential evolution algorithm to track the globalMPP under partial shading conditions. According to the authors,simulation results show that DE can track MPP very fast andaccurately. Tumbelaka and Miyatake[118]have proposed a three

phase, four-wire controlled voltage source inverter for both powerquality improvement and SPV energy extraction. The MPPTcontroller employs the PSO technique. From computer simulationresults, it proves that grid currents are sinusoidal and in phase with

grid voltages, delivering maximum power to the loads.

Table 7

Summary of applications of evolutionary algorithms (GA, PSO and ACO) for MPP

tracking in PV systems.


1 Chen et al. [116] 2010 Photovoltaic module under

uniform insolation

2 Taheri et al. [117] 2010 Grid-connected PV systems


conditions3 Tumbelaka

and Miyatake

[118] 2010 A grid current-controlled

inverter with particleswarm optimization

MPPT for PV generators

4 Miyatake et al. [119] 2011 Multiple photovoltaic

arrays under partial shading

conditions/grid connected

5 Ngan and Tan [120] 2011 Multiple peaks tracking

algorithm using PSO incorporated

with articial neural network

6 Ishaque et al. [121] 2012 Photovoltaic generating system

under partial shading conditions7 Liu et al. [122] 2012 PSO-Based MPPT under partial

shading conditions

8 Ishaque et al. [123] 2012 A direct control based maximum

power point tracking method

for photovoltaic system underpartial shading conditions using

PSO algorithm

9 I shaque

and Salam

[124] 2013 A deterministic particle swarm

optimization MPPT for PV

systems under partial shadingconditions

10 Jiang et al. [125] 2013 A novel ant colony

optimization-based maximumpower point tracking for

photovoltaic systems under

partial shading conditions

12 Adly and

Besheer

[126] 2013 ACO with PID and fuzzy logic

for MPPT in stand-alone PV systems

13 Shaiek et al. [127] 2013 Shading solar photovoltaic

generators

Cal

U

No

No

Yes

St

PSO in

j

lculate the fitness

Betterind

fitness v

Bettergless v

All paeval

Fitness

Update particles v

End

Convercriterion

Fitness v

Yes

tart

nitialization

j=1

s value of particle

dividual

value ?

lobalfitnalue ?

articlesuated

s value ?

elocity and positi

d

rgencen met?

value?

No

No

Yes

ej

ion

Yes

Updatepbest

Updategbest

Nextparticle

j=j+1

Nextiteration

i=i+1

Fig. 13. Flowchart of a standard PSO [115].



13/21

Miyatake et al.[119]attempted to approach the GMPP using thePSO algorithm. In these investigations, the authors try to realize

centralized MPPT control of the modular (multi-module) PowerGenerating System (PGS). These MPPT algorithms have good per-formance under various partial shading conditions (PSC); however,these methods are only suitable for systems that consist of multiple

converters. However, for PGS, the use of one central high-powersingle-stage electronic converter is very common for economicreasons and the relative simplicity of the overall system.

A hybrid algorithm of PSO and ANN MPPT algorithm to track the

GMPP is proposed in Ref. [120]. The ANN was used to generatesuitable values ofDPand initial value of PV current to the PSO al-gorithm, when there is a change in solar irradiance. Then PSO al-gorithm generates the corresponding PV current at MPP.

Simulation results show that the proposed algorithm performs wellto detect the true global peak power. An improved PSO-based MPPTalgorithm for PGS is developed by Ishaque et al. [121]. The resultsindicate that the proposed controller outperforms the Hill Climbing

(HC) and gives a number of advantages: it has a faster trackingspeed; it exhibits zero oscillations at the MPP; it could locate theMPP for any environmental variations including partial shadingcondition and large uctuations of insolation; and the algorithm

can easily be developed using a low-cost microcontrollers. Liu et al.[122]modied the standard version of PSO to meet the practical

consideration of PGS operating under PSC. The proposed methodboasts the advantages such as easy to implement, system-

independent, and high tracking efciency. An MPPT algorithmbased PSO to track the global MPP under partial shading conditionsis developed by Ishaque et al.[123], the advantage of the method isthat it does not need additional control loops. According to the

author, the method performs excellently under all shadingconditions.

To improve the efciency of the MPPT capability for PV systemunder PSC a deterministic PSO technique is designed in Ref. [124].

Simulation results indicate that the proposed method outperformsthe HC method in terms of global peak tracking speed and accuracyunder various partial shading conditions. Furthermore, it is testedusing the measured data of a tropical cloudy day, which includes

rapid movement of the passing clouds and partial shading. Despitethe wide uctuations in array power, the average efciency for the10-h test prole reaches 99.5%. Jiang et al. [125]proposed a ACO-based MPPT scheme for PV systems. A new control scheme is also

introduced based on the proposed MPPT method. This heuristicalgorithm based technique not only ensures the ability to nd theglobal MPP, but also gives a simpler control scheme and lower sys-tem cost. Adly and Besheer [126] have used ant system optimization

to improve both the design efciency of PI and fuzzy control sys-tems. It has been shown that the developed MPP tracking techniqueenhances the initial starting point of the traditional tracking algo-rithms and it could be used under variable irradiance levels. Shaiek

et al. [127]have used a GA to track the global MPP under partialshading conditions using PowerSim toolbox of MATLAB, and makingcomparison with two classical techniques. The results indicate that

the GA can track the GMPP successfully. Other intelligent search

methods can be found in Refs. [128,129].

4.6. Application of hybrid methods for MPP tracking

Some applications of hybrid approaches which combine twomethods(intelligent or classical methods) areillustrated inTable8. As

example, Fig.14shows a simplied schematic fuzzy logic with MPPTclassical search algorithms (IC, P&O, HC or other effective searchtechniques), however, the search algorithm could also be replaced byANN or ANFIS to estimate the reference voltageVrefor MPPref.

Mozaffari et al. [130] employed the incremental conductance(IC) method which has been improved by using FL. The proposedFL-IC MPPT scheme provides enough modication to the conven-tional IC method to enjoy an appropriate variable step size MPPT

control signal for the Z-source inverter (ZSI). The developedmethod is used for a water pumping system. An intelligentcontroller (RBFN and ENN) is developed to extract the maximumpower from a stand-alone hybrid system (solar-wind). An efcient

power sharing technique, among energy sources, are successfullydemonstrated and showed more efciency, a better transient andmore stability, even under disturbance [131]. Alajmi et al. [132]used the Hill-Climbing search method, which has been modied

Table 8

Summary of applications hybrid methods for MPP tracking in PV systems.


1 Mozaffari et al. [130] 2011 DC motor drives supplied

by PV power system

2 Lin et al. [131] 2011 Stand-alone hybrid power

generation system

3 Alajmi et al . [132] 2011 Microgrid stand-alone

photovoltaic system4 Jinbang et al. [133] 2011 Photovoltaic system

generation5 Sheraz and Abido [134] 2012 Photovoltaic systems

Fig. 14. Example of a block diagram of MPPT-based hybrid methods.



14/21

based on fuzzy logic control for MPPT under rapidly changingweather conditions. Fuzzy-logic based Hill Climbing offers fast andaccurate converging to the maximum operating point duringsteady-state and varying weather conditions compared to con-

ventional HC. Jinbang et al. [133]have developed a hybrid tech-nique using an ANN and the conventional IC algorithm to track theMPP for a photovoltaic system generation. Simulation results areshown to verify the effectiveness of the proposed method. A hybrid

method based on DE and ANN was introduced in Ref.[134]to trackthe MPP in partial shading conditions. Simulation results showedthat the proposed MPPT-controller could track the MPP in less timecompared to conventional MPP methods and without any uctu-

ation in steady state.

5. FPGA-based implementation of intelligent MPPT

controllers

Developing and producing a digital system is a complicated

process and involves many tasks. The best way to handle thecomplexity is to view the circuit at a more abstract level and utilizesoftware toolsto derive the low-levelimplementation [62].Acritical

step in the design of any electronic product is the nal verication.Therefore, the designer must take some action to assure that theproduct, once in production, will perform to its specication. Thereare two general ways to do this: prototyping and simulation[135].

- Prototyping

The most obvious and traditional method of design vericationis prototyping. A prototype is a physical approximation of the nal

product. The prototype is tested through operation and

measurement

- Simulation

Simulationattemptsto create a virtual prototype by collecting asmuch information as is known or considered pertinent about thecomponents used in the design and the way they are connected.

The majority of the MPPT-based AI techniques aredemonstratedbased on simulation results, except some hardwarevalidation usingmicrocontrollers and DSPs [74,121,136e138]. However, due to theadvantages of recongurable chips such as FPGA reported before,

MPPT-based fuzzy-logic, fuzzy logic optimized GA, adaptive neuro-fuzzy inference system and neural network controllers have beenrecently designed and implemented into recongurable FPGAs[46,76,139e150], in order to improve the efciency of the photo-

voltaic system.High-level design tools are becoming popular for designing

using FPGAs. There are generally three ways to convert thedesigned MPPT algorithm to a code source adequate for FPGAs:

The rst is to use directly a hardware description language suchas VHDL or Verilog; however, this method is not easy as moreknowledge about soft-computing and hardware language arerequired specially in the case of intelligent techniques, which is a

serious barrier for designers and engineers.The second way, whichis veryeasy, includes the use of MATLAB/

Simulink, such as system generator [58] from Xilinx that allowsXilinx chips to be programmed within the common MATLAB

Fig. 15. Block diagram of intelligent MPPT controller under FPGA [46].

Fig. 16. Floor planning of the FPGA implementing the MPPT controller and interface

circuits (the used space (area) in the FPGAVirtex II V2MB1000 development kit) [46]. Fig. 17. The designed DCe

DC boost converter with data-acquisition system[147].



15/21

programming environment. The system generator automaticallycompiles designs into low-level representations. Experiments us-ing hardware generation can suggest the hardware speeds that arepossible and, through the resource estimation, give a rough idea of

the cost of the design in hardware. If a promising approach isidentied, system generator can create the bit stream (physicallevel) to the FPGA chip. It can also generate equivalent represen-tations of the design, at the same or lower level, and furthermore it

is an equivalent high-level module that performs a specic functionin applications external to system generator (ModelSim hardwareco-simulation)[58].

The third way consist to use another high-level design tool such

as DK2[151] from Celoxica and Forge[58]from Xilinx, which usehigh-level languages such as C and Java to develop the code sourcefor FPGA. This way is also not easy as it needs more knowledge andskills and it takes more time.

As example, Fig. 15depicts the block diagram of FL-based GAcontroller for tracking the MPP in a PV module. This controller wasdesigned using VHDL integrated with the Xilinx Foundation ISE 7.1itools.

The used space (area) for the embedded MPPT-based FL-GAcontroller into a Virtex II V2MB1000 development kit [46] is

illustrated inFig.16. As can be observed this chip is large enough toimplement this controller.

Fig. 17shows the designed DCeDC boost converter with data-acquisition system for measuring photovoltaic current and

voltage (Iin, Vin, Vout and Iout). While Fig. 18 depicts the corre-sponding electronics circuit using Eagle software.

Fig. 19shows the simulated system under MATLAB/Simulink;the system includes PV module, DCeDC converter, PWM generator,

MPPT algorithm based on GA-FL and a resistive load.Other results about the implementation of intelligent MPPTs

into FPGA are given in Ref. [149]. For example, Fig. 20(a) and (b)shows the FPGAVirtexV ML501-XC5VLX50 and the duty cycle

generated by the designed FL-GA controller [149], which is dis-played in PWM form with a scope under constant conditions, usingreal measuredIeVcurve atG 700 W/m2,T24 C. This is thenal step, which conrms the implementation of designed intelli-

gent controllers into FPGA chip. It should be noted that theemployed VirtexV is more powerful compared to Virtex II used inour previously designed controllers; it is also very fast and hasmore memory space and facilities.

Simulation results usingModelSimsoftware of the MPPT basedFL-GA for rapid variation of solar irradiance and air temperature is

Fig. 18. The Electronics circuit of the designed DCeDC with data-acquisition system reported inFig. 17[147].

Fig. 19. MATLAB/Simulink of the system (PV module DCe

DC converter, PWM generator and GA-FL MPPT controller and load) [147,148].



16/21

shown inFig. 21. Simulation results show that the designed tech-nique performs better under variable conditions.

As an example, Table 9 reports a comparison between four

intelligent MPPT controllers[149].Results reported in Refs. [46,76,139e150] indicate that good

effectiveness of the implemented MPP-intelligent controllers isobserved and demonstrate the feasibility of implementing such

techniques in real-time applications. Some of them are tested

experimentally[150]. However, hardware development of a pro-totype for commercial applications is not yet achieved. We believein the future more efforts will be done to make a commercial

prototype of such embedded intelligent MPPT controllers.Table 10 summarizes the implemented intelligent MPPT con-

trollers into FPGA. It is important to point that the FPGA chip shows

opportunities for improved performance and design exibility fordigital control of MPP in photovoltaic systems. The most imple-mented intelligent MPPTs are based on: fuzzy logic, neuralnetwork, FL-genetic algorithm, ANFIS and other hybrid techniques

which combine a classical method with one AI technique, e.g. HCand fuzzy logic.

Cheng et al. [139] implemented a fuzzy MPPT algorithm intoFPGA (Altera Corporation CycloneII chips EP2C8Q208C8), experi-

mental veri

cation show that the method is more quick and

accurate than P&O algorithm and does not cause any oscillationnear the maximum power. A combination method between ANNand IncCond algorithm is proposed and implemented into an FPGA

chip (FPGAVirtex II Prof of Xilinx) [140]. The ANN increases thetracking time of the IncCond by guiding the DCeDC converter tooptimal voltage immediately, and then the IncCond tracks the exactMPP and helps ANN not be trained periodically. Messai et al. [76]

described the hardware implementation of a two-inputs one-

output digital FLC on a XilinxFPGA chip. The simulation resultsobtained with ModelSim Xilinx Edition-III show a satisfactoryperformance with a good agreement between the expected and the

obtained values. In another work Messai et al. [46]improved theefciency of the designed FLC-MPPT using a genetic algorithm, andthen the method has been implemented into an FPGA; results

conrm the good tracking efciency and rapid response to changesin environmental parameters.

Chekired et al.[141]have developed an MPPT method based onfuzzy logic, and then the designed method was implemented into

FPGAVirtex II. Results show that the proposed MPPT controller isfaster in transient state and presents smoother signal with lessoscillation in steady state. An adaptive fuzzy MPPT controller isdesigned and implemented into FPGA (Altera Corporation Cyclone

II series chip EP2C8Q208C8) [142]. Experiments proved that the

Fig. 20. (a) The FPGA chip used for co-simulation, (b) The duty cycle D generated by the FL-GA controller, displayed in PWM form under constant conditions (G 700 W/m2,

Ta 24 C) using real measured IeVcurve[149].

Fig. 21. The evolution of the PV power and duty cycle vs. time of the designed FL-GA for rapid variation of solar irradiance and air temperature (G 700 W/m2,

Ta 24 C/ G 300 W/m2, Ta 19

C/ G 700 W/m2, Ta 24 C)[149].



17/21

controller using this method can adjust the parameter according tothe change of the external condition with good accuracy.

Two intelligent MPPT controllers have been designed and

implemented on FPGA by Chekired et al.[143]. The implementedcontrollers are neuro-fuzzy and FL. Both MPPT controllers havebeen evaluated and compared in real-time simulation. The imple-mentation of intelligent controllers into FPGA for the tracking of the

MPP is very promising. They achieved very good performances, fastresponses without overshoot and less uctuation in the steadystate, for rapid variation of atmospheric conditions.

To increase the energy generation efciency of the solar cells,

Hamed and El-Moghany [144] proposed the MPPT using fuzzycontrol and implemented into an FPGA card (Spartan-3AN, Xilinx

Company). A BuckDCe

DC converteris used for this application. Thecomparison shows that the FLC is better in response and does not

depend on the knowledge of any parameter of the PV panel. Theinformation required for fuzzy control is only the generated power,therefore, the hardware is simple and the cost of this system isinexpensive.

A sun tracking generating power system is designed andimplemented in real time using a fuzzy logic [145]. A fuzzy logiccontroller is implemented into Spartan-3AN. The proposed FLCshows an excellent result. It is shown that the sun tracking system

using fuzzy controller with FPGA technology is 24% more energyefcient than a xed sun panel system.

Punitha et al.[146]used a modied IncCond and fuzzy logic totrack the MPP of a PV system under the partial shading and varying

atmospheric conditions. The designed controller is implementedinto Xilinx spartran-3 FPGA. The experimental results show satis-factory performance of the proposed approaches. Another twoapplications on Photovoltaic nodule with DCeDC converter are

presented by Messai[147]and Messai and Mellit[148].Four intelligent methods for tracking the MPP in photovoltaic

systems have been designed in order to improve the efciency of

PV systems under variable weather conditions (air temperature andsolar irradiance) [149]. The effectiveness of these methods has beenevaluated with different simulation studies under MATLAB/Simu-link and ModelSim. The advantages of the intelligent methods-

based MPPT controller are: they offer an alternative approach toconventional MPPT controllers; they exhibit a faster convergingspeed, good performance, better efciency, less oscillation around

the MPP under steady-state conditions, and no divergence from theMPP during varying weather conditions.

Punitha et al.[150]also developed a method using IncCond al-gorithm and an ANN to track the MPP under partial shading con-dition. The developed ANN was used to supply the voltage Vreftothe modied IncCond method. The designed approach was imple-mented into FPGA to validate the results from the hardware setup.

6. Comments and remarks

With respect to the above reviewed papers, which include thelatest research work, the following key conclusions can be made:

The advantages of the intelligent methods based MPPT con-

trollers are that they exhibit a faster converging speed, good

performance and efciency, less oscillations around the MPPunder steady-state conditions, and no divergence from the MPPduring varying weather conditions.

MPPT-based intelligent methods such as fuzzy-logic, neuralnetworks (MLP, RBF, etc.) and neuro-fuzzy could not be used inthe case of partial shading conditions. These techniques shouldbe modied or combined with other advanced search algo-rithms or one of AI techniques (e.g., ANN with PID controller,

fuzzy logic with adaptive search algorithm, fuzzy logic withANN, etc.). These techniques can be implemented into FPGAchips. However, the required FPGA memory space/area and thesimplicity implementation depend mainly to the nal structure

of the developed controller. Generally, Virtex II or Spartan-3E

chips are sufcient to implement the methods[67,149]. Never-theless, additional effort and more knowledge on FPGAsconguration are indispensible to optimize the space memory(area) inside FPGA.

The ANN controller, under uniform insolation and PV array builtof the same PV modules technology, can provide good results.The main advantages of this technique are that no detailed in-

formation about the system is required, are easy to be imple-mented and requires relatively less memory space. However, itneeds a heuristic sense and it works as a black box. In addition,its robustness depends on the good training parameters.

Although, good performance has been demonstrated in theliterature, for rapid variation of weather conditions, the methodhas some drawbacks [152]. The main drawback of this technique

is that it could fail when the PV modules start to be degraded, atapproximately 10%, especially for thin lm solar cells (CIGS,CdTe, etc.); in this situation, training with

Documents

1_ANN&GA-MPPT-based Artificial Intelligence Techniques for Photovoltaic Systems