applications of fuzzy logic in renewable energy systems – a review

Applications of fuzzy logic in renewable energy systems A review

L. Suganthi a,n, S. Iniyan b, Anand A. Samuel c

a Department of Management Studies, College of Engineering, Anna University, Chennai 600025, Indiab Institute for Energy Studies, Department of Mechanical Engineering, College of Engineering, Anna University, Chennai 600025, Indiac VIT University, Vellore 632014, India

a r t i c l e i n f o

Article history:Received 9 November 2013Received in revised form19 March 2015Accepted 3 April 2015Available online 28 April 2015

Keywords:Fuzzy logicNeuro-fuzzyANFISFuzzy AHPFuzzy MCDM

a b s t r a c t

In recent years, with the advent of globalization, the world is witnessing a steep rise in its energyconsumption. The world is transforming itself into an industrial and knowledge society from anagricultural one which in turn makes the growth, energy intensive resulting in emissions. Energymodeling and energy planning is vital for the future economic prosperity and environmental security.Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted inenergy modeling to precisely map the energy systems. In this paper, an attempt has been made to reviewthe applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used inrecent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solarphotovoltaic/wind, optimization among conicting criteria. The review indicates that fuzzy basedmodels provide realistic estimates.

& 2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5852. Energy models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5863. Fuzzy logic models in energy systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586

3.1. Fuzzy models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5863.2. Hybrid models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5873.3. Multicriteria decision models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

4. Applications of fuzzy logic in renewable energy systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5884.1. Solar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5904.2. Wind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5954.3. Bio-energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5984.4. Hybrid systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600

5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603

1. Introduction

Energy plays an important role in the development of anycountry. The source of energy is dominated by fossil fuels. The20th century witnessed a rapid twenty fold increase in fossil fuelconsumption. Among the fuels, coal and oil are being used for

electricity generation which in turn is used for lighting, heatingand cooling. Oil is used in transportation, heating and cooling.According to International Energy Agency (IEA) data from 1990 to2008, the energy consumption per capita increased by 10% whilethe world population increased by 27%. This was mainly due to thegrowth in China, India and the Middle East [1]. The growth inenergy consumption in the G20 countries slowed down to 2%in 2011.

Global warming resulting from extensive fossil fuel consump-tion has now become a global phenomenon. If developing countries

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Renewable and Sustainable Energy Reviews

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n Corresponding author. Tel.: 91 98412 44331.E-mail address: [email protected] (L. Suganthi).

Renewable and Sustainable Energy Reviews 48 (2015) 585607

especially India and China are able to efciently use the energyand bring in a steep declining trend in its energy intensity, it willbenet the respective country, their economy as well as the globalenvironment.

About 16% of the global energy consumption comes fromrenewable energy resources. The share of renewable energy inelectricity generation is around 19%, with 16% from hydro and 3%from renewable energy resources. Renewable energy is environ-ment friendly and renewable in nature. From the end of 2004,worldwide renewable energy capacity grew at rates of 1060%annually for various technologies [2]. In 2010, renewable powerconstituted about a third of the newly built power generationcapacities [3]. According to a 2011 projection by the IEA, solarpower generators may produce most of the world's electricitywithin the next 50 years. Renewable energy technologies aregetting cheaper, through technological change and through thebenets of mass production and market competition. If policiessuch as environmental tariff, net metering, emissions trading,carbon tax, carbon emission trading are introduced, the utilizationof renewable energy can be further increased. Thus it is seen thatenergy planning is the need of the hour.

2. Energy models

Proper planning is required at the global, national and regionallevel to handle the energy consumption on one hand and con-sequent emissions on the other. The world has been endowed withenvironment friendly renewable energy sources. Academicians,policy makers and industrialists need to work in tandem fordeveloping energy models to circumvent the growing energy-environment predicament.

Energy models are developed for sustainable development ofany nation. Advanced energy models are required for handlingcomplex phenomena. There are a variety of models used in energymodeling. Time series, regression, ARIMA, inputoutput, decom-position models, grey prediction, co-integration models are usedfor energy demand forecasting. Integrated energy planning modelstry and balance the energy supply with energy demand. Articialintelligent systems namely neural network, fuzzy logic, geneticalgorithm are being used for building optimization models.

Energy models also help in integrated assessment consideringavailability, potential, economics, emission, technology, socialacceptance etc. Econometric models deal with the costs ofresources, energy systems, economy of the country, technologicalparameters etc. Energy-economic models are used to examine thewider impacts of changes in the energy system and the economy.Energy system models are technology based cost optimizingmodels that are predominantly used for technology assessments.Energy-environment models help in understanding the linkagebetween energy use, greenhouse gas emissions and climatechange. With several competing energy options, energy modelsbased on fuzzy logic enable researchers to realistically select theright mix of energy resources considering the various conictingcriteria like costs, availability, emission etc.

3. Fuzzy logic models in energy systems

Energy projects in developing countries have proved that renew-able energy can directly contribute to poverty alleviation as well asprovide for business and employment opportunities. However elec-tricity from renewable energy is argued to be intermittent and henceunreliable. On the one hand consumers need to be educated on thenecessity of using non-polluting cleaner energy sources while thepolicy makers in the government must bring in legislations and

reallocations in the budget spending so as to enhance renewableenergy utilization to a greater extent. If realistic models are devel-oped to provide reliable, environment friendly energy from home-spun commercial and renewable energy sources it would aid theglobal community and leave behind a healthy environment forgenerations to come. Fuzzy logic helps in conceptualizing thefuzziness in the system into a crisp quantiable parameter. Thusfuzzy logic based models can be adopted for effective energyplanning to arrive at pragmatic solutions.

Fuzzy logic deals with reality and it is a form of many valuedlogic. It deals with reasoning that is approximate having alsolinguistic values rather than crisp values. Fuzzy logic handles theconcept of truth value that ranges between completely true andcompletely false (01). Fuzzy logic has been applied in manydisciplines. Fuzzy logic and probability are different ways ofexpressing uncertainty. Fuzzy set theory used the concept of fuzzyset membership while probability theory uses the concept ofsubjective probability. The various types of membership functionsnormally used in fuzzy logic are triangular, trapezoidal, Lfunction, function, S function, Gaussian fuzzy set. All of thesefunctions can be used in the modeling of energy systems.

Fuzzy logic based models in energy systems can range from themost simple to the most complex. They can be broadly classied asfollows:

(i) Fuzzy models(a) fuzzy delphi(b) fuzzy regression(c) fuzzy grey prediction(d) fuzzy AHP(e) fuzzy ANP(f) fuzzy clustering(ii) Hybrid models

(a) neuro-fuzzy, adaptive neuro-fuzzy inference system(ANFIS)

(b) fuzzy genetic algorithm, neuro-fuzzy GA(c) fuzzy expert system, neuro-fuzzy expert system(d) fuzzy DSS(e) fuzzy DEA, neuro-fuzzy DEA

(iii) Multi criteria decision models(a) fuzzy VIKOR(b) fuzzy TOPSIS(c) fuzzy support vector machine(d) fuzzy particle swarm optimization(e) fuzzy honey bee optimization(f) fuzzy cuckoo search optimization(g) fuzzy quantum particle swarm optimization(h) fuzzy ant colony optimization.

3.1. Fuzzy models

The fuzzy logic based models are being used extensively becauseit maps the realistic situation to a large extent. Fuzzy delphi methodis used when the experts' response is of a fuzzy nature. Severalrounds are conducted among experts to arrive at a consensus. Infuzzy regression, the data for the dependent and independentvariables are captured in a fuzzy manner and the effect of theindependent variables on the dependent variable is determinedusing the derived regression equation. In fuzzy grey prediction,similar to regression approach, the fuzziness is used to capture thegrey area in the variables considered for the dependency prediction.Fuzzy AHP and fuzzy ANP are used to nd the relative importance ofthe variables. Fuzzy approach helps in accurately capturing thefuzziness in the minds of people while ranking the variables. Infuzzy clustering, the range given to the variables helps in clearly

L. Suganthi et al. / Renewable and Sustainable Energy Reviews 48 (2015) 585607586

demarcating the clusters and draw boundaries. Each of theseapproaches are being used depending on the problem domain. Forprediction purposes, fuzzy delphi, fuzzy regression, fuzzy greyprediction can be used. Fuzzy AHP, fuzzy ANP can be used for ndingthe relative importance of the energy resources. Fuzzy clustering isused for grouping of resources based on selected criteria such as cost,availability, pollution etc. These techniques can be classied assimple considering its complexity involved in the technique andcan be used for prediction or grading the importance of energysources/systems.

3.2. Hybrid models

Neuro-fuzzy models and ANFIS models have been used exten-sively during the past decade especially in control systems. As seen

from Table 1, extensive research has been done in neuro-fuzzy andANFIS models. In recent years it is found that fuzzy inferencesystems are used extensively in solar PV control/smart gridsystems. Fuzzy genetic algorithm is also used in control systemsfor solar PV/wind and for nding the best wind energy generationterrain. Fuzzy expert system and neuro-fuzzy expert systems arearticial intelligent systems used for identifying the best energyresource or for maximizing the available resource. Fuzzy DSS usesa combination of various decision models. It helps to identify thedecision model given an energy situation. Fuzzy DEA is anoptimization technique which helps in determining the bestcombination of resources that can be used considering the variousconstraints present in a situation/region. These models may beclassied as medium in complexity. They can be used foraccurately simulating the system and/or its performance. However

Table 1Fuzzy models in renewable energy systems.

Techniques Area ofresearch

Researchers

Fuzzy regression Solar Martin et al. [47], Ramedani et al. [48]Wind An et al. [49], Kaneko et al. [50], Wang and Xiong [51]

Neuro-fuzzy, ANFIS Renewables Mamlook et al. [9]Solar Alata et al. [52], Arulmurugan and Suthanthiravanitha [53], Cao and Lin [54], Chaabene and Ammar [55], Chaouachi et al.

[56], Chen et al. [57], Kharb et al. [58], Landeras et al. [59], Lee et al. [60], Mellit et al. [61], Mellit and Kalogirou [62], Salahand Ouali [63], Sfetsos and Coonick [64], Syafaruddin et al. [65], Zarzalejo et al. [66]

Wind Capovilla [67], De Giorgi et al. [68], Haque et al. [69], Hong et al. [70], Jafarian and Ranjbar [71], Lin et al. [72,73], Liu et al.[74], Mohandes et al. [75], Monfared et al. [76], Osorio et al. [7780], Pousinho et al. [81], Sfetsos [82], Shamshirband et al.[83,84], Yang et al. [85]

Bio-energy Jurado and Saenz [86], Jurado et al. [87]Hybrid Rajkumar et al. [88]

Fuzzy AHP, ANP Renewables Heo et al. [22], Kahraman et al. [20], Ren and Sovacool [24], Tarsri and Susilawati [23]Solar Lee et al. [60]Wind Lee et al. [89], Liu et al. [90], Shaee [91], Yeh and Huang [92]Bio-energy Chang et al. [93]Hybrid Chen et al. [94]Space heating Jaber et al. [17]

Fuzzy clustering Renewables Zare and Niknam [44]Fuel cell/solar/wind

Niknam et al. [40,41]

Solar Gomez and Casanovas [95]Wind Azizipanah-Abarghooee et al. [96], Calderaro et al. [97], Ugranli and Karatepe [98], Ustuntas and Sahin [99]Bio-energy Ayoub et al. [100]

Fuzzy optimization Renewables Ho et al. [12], Li et al. [13], Vahidinasab [14]Solar Benlarbi et al. [101]Wind Liang et al. [102]Bio-energy Balaman and Selim [103], Ng et al. [104], Tan et al. [105]Hybrid Malekpour et al. [106]

Neuro-Fuzzy DEA Solar Azadeh et al. [107]Wind Azadeh et al. [108]

Fuzzy GA Solar Kisi [109], Luk et al. [110]Wind Gonzlez de la Rosa et al. [111]Hybrid Ansari and Velusami [112,113], Kalantar and Mousavi [114]

Neuro-Fuzzy GA Solar Chekired et al. [115]Wind Kasiri et al. [116]

Fuzzy expert Renewables Kaminaris et al. [7], Kyriakarakos et al. [8]Wind Zhao et al. [117]Buildingdesign

Dounis et al. [16]

Neuro-fuzzy expert Bio-energy Romeo and Gareta [118]Fuzzy MCDM Renewables Agrawal and Singh [10]

Solar Charabi an Gastli [119], Salah et al. [120], Wu et al. [121]Wind Lee et al. [89]Bio-energy Bitar et al. [122], Ren et al. [123]

Fuzzy TOPSIS, VIKOR, Renewables Kaya and Kahraman [21], Sengul et al. [42]Solar Ahmadi et al. [124,125], Cavallaro [126]Hybrid Perera et al. [127]

Fuzzy PSO Micro-grid Moghaddam et al. [31,32]Solar Sangawong and Ngamroo [128], Welch and Venayagamoorthy [129], Zeng et al. [130]Wind Aghaei et al. [131], Bahmani-Firouzi et al. [132], Liang et al. [133], Lin et al. [73], Pousinho et al. [81], Wang and Singh [134]

Fuzzy honey beeoptimization

Renewable Niknam et al. [3941]

Fuzzy PSO, QPSO, Cuckoooptimization

Hybrid Berrazouane and Mohammedi [135], Bigdeli [136]

L. Suganthi et al. / Renewable and Sustainable Energy Reviews 48 (2015) 585607 587

use of these models can be undertaken only if the accuracy jus-ties the cost and complexity.

3.3. Multicriteria decision models

With the latest advancements taking place in informationtechnology sector, the multicriteria decision models have gainedmomentum. These techniques require the intense computation.Fuzzy VIKOR and fuzzy TOPSIS are used for renewable energyoptimization. Specialized softwares are being used to arrive ataccurate conclusions. Support vector machine, particle swarmoptimization, quantum particle swarm optimization, honey beeoptimization, cuckoo search optimization, ant colony optimizationare all machine learning tools which helps to unravel the mysterybehind the data and accurately predict the possible outcomes.These are now being used in renewable energy sector for controlsystems, grid applications, emission reduction, to name a few. Thefuzzy based MCDM techniques are classied as complex since itrequires specialists to run the packages and interpret the results. Itis predominantly being used to test models using numericalanalysis and simulation studies.

A review is carried out on the various types of fuzzy modelsapplied to renewable energy systems.

4. Applications of fuzzy logic in renewable energy systems

Groscurth and Kress [4] have adopted fuzzy set theory andfuzzy c-means clustering to compress the data which is used inenergy optimization for minimizing the primary energy inputs andemission of pollutants. Cai et al. [5] have identied optimalstrategies in the planning of energy management systems undermultiple uncertainties using fuzzy-random interval programmingmodel. The method integrated interval linear programming, fuzzystochastic programming and mixed integer programming. Fuzzylogic has thus helped in effectively capturing and compressing thedata and uncertainties present in energy modelling.

Ludwig [6] has used a novel fuzzy based method for assessingthe various energy conversion technologies taking into accountthe environmental aspects and have concluded that renewableenergy needs to utilized for sustainability. Kaminaris et al. [7] havedeveloped a fuzzy based expert system model to determine anunique fuzzy project priority index for prioritizing Renewables-to-Electricity system. The variables consider the life cycle analysis andhence this priority index will be highly useful to practitioners tochoose a renewable energy based electricity system. Kyriakarakoset al. [8] have presented the design and implementation of fuzzycognitive maps based decision support toolkit for renewableenergy systems planning and has tested it in Crete Island. Thistoolkit will be very useful for decision makers to critically evaluatetheir investments in renewable energy systems in local commu-nities. It is found that fuzzy logic is also being used extensively asan assessment/evaluation tool.

When various options are thrown open to policy makers, itbecomes imperative to rationally and optimally choose the bestresource considering several constraining factors. Fuzzy logic basedmodels have emerged as an effective tool to meet this need.Mamlook et al. [9] have used a neuro-fuzzy program to assess thedifferent electricity power generation options for Jordan. Theoptions considered are fossil fuel, nuclear, solar, wind and hydro-power systems. Based on the cost-to-benet ratios, the resultsindicate that solar, wind and hydropower are the best options whilenuclear and fossil fuel based electric power are the least preferred.The energy allocations for cooking considering economic, environ-mental, technical factors have been determined by linear fuzzica-tion of multiple objectives [10]. LPG, biogas, fuelwood, solar thermal

energy, grid electricity are considered in the model. A new conceptoperational adequacy measure as a measure of the degree ofsatisfaction has been introduced in the optimization model. El-Tamaly and Mohammed [11] have determined the impact ofinterconnecting photovoltaic/wind energy system from reliabilitypoint of view using fuzzy logic based reliability index for eachhybrid electric power system.

Fuzzy based multiobjective optimization models are being used tond the best combination of resources to be used in a constrainingenvironment. A multiobjective linear programming model has beendeveloped by Ho et al. [12]. It uses a fuzzy two stage algorithm forenergy conservation and increased renewable energy utilization so asto reduce CO2 emission. This model can be used for selecting a lowcarbon energy conservation system in buildings and campuses. Trade-offs among system costs, energy utilization and GHG emission controlwere determined by Li et al. [13] using a fuzzy dual-interval multi-stage stochastic (FDMSP) optimization framework. The model helps toeffectively balance between system costs, energy utilization and GHGemissions to arrive at optimal renewable/non-renewable energysolutions. Vahidinasab [14] have presented a probabilistic multiobjec-tive optimization model for distributed energy resources (DER) (whichincludes wind turbine, photovoltaic, fuel cell, micro-turbine, gasturbine and diesel engine) planning in electricity networks. A hybridfuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used forscenario based modeling of the electricity prices. Veena et al. [15] hasdone a review of grid integrated schemes for renewable powergeneration system including fuzzy logic control mechanism.

Fuzzy expert systems and fuzzy based hierarchical processinghave been adopted in buildings to increase the comfort level withminimum energy use resulting in passive building designs. Douniset al. [16] have proposed a fuzzy logic expert system in the designof solar buildings to achieve optimal thermal and visual comfortconditions within the living and working space. Jaber et al. [17]using benet to cost ratio from fuzzy AHP evaluated conventionaland renewable energy based space heating systems using multi-criteria analysis. It was found renewable energy (namely solar andwind) based heating system was more favorable. Yu and Dexter[18] have used hierarchical fuzzy supervisory controller for opti-mizing the operation of a low energy building which uses solarenergy for heating and cooling the interiors.

Cai et al. [19] have developed an interval-parameter super-iorityinferiority based two-stage programming model for sup-porting community-scale renewable energy management (ISITSP-CREM) considering system cost, reliability and energy security. Themethod integrates interval linear programming (ILP), two-stageprogramming (TSP) with superiorityinferiority based fuzzy-stochastic programming (SI-FSP). Such integrated models will behighly useful in analysing competing scenarios to determinevarious decision alternatives.

Kahraman et al. [20] have utilized multi-criteria decisionmaking methodologies and found wind to be the best alternativefor Turkey considering the four main criteria economic, environ-ment, socio-political and technological. The uniqueness of theirresearch is in the use of fuzzy axiomatic design and fuzzy AHP inthe selection of alternatives from among the renewable energysources. Kaya and Kahraman [21] have determined the bestrenewable energy alternative for Istanbul by using an integratedfuzzy VIKOR and AHP methodology. The same approach is alsoused to determine the energy production sites. The uncertainty(fuzziness) in human preferences creates vagueness in the deci-sion making process. This is overcome using the proposed fuzzy-VIKORAHP methodology which clearly brings out the relativeimportance score for each resource.

Fuzzy AHP using fuzziness in the range values determine therelative importance scores of various renewable energy dissemi-nation programs in Korea based on ve criteria technological,


market-related, economic, environmental, and policy-related and 17 factors [22]. Fuzzy AHP has been used in the selection ofthe best renewable energy source for electricity generation inIndonesia [23]. Ren and Sovacool [24] have used fuzzy AHP todetermine the relative weights indicating the importance ofenergy security factors in China. Such range values adopted infuzzy based models help in arriving at quantiable crisp scores.

An integrated modeling system (IMS) has been developed forplanning of the energy management system and the climatechange impact analysis in the Province of Manitoba, Canada[25]. The fuzzy reasoning introduced in their model helped invisualizing the impact of climate change on energy managementsystems. It also helped in proposing adaptation measures.

Zheng et al. [26] have compared the prediction models such asKalman ltering, data mining, wavelet transform and articialintelligence such as neural networks, fuzzy inference and biologi-cal intelligence algorithm, their forecasting versatility for windand PV power generation. Fuzzy logic technique has not only beenused in identifying alternatives, in forecasting but also in thecontrol devices in energy systems. Sakhare et al. [27] have triedpower conditioning of fuel cell using fuzzy logic control forstandalone systems and grid connections. Hanmandlu and Goyal[28] have proposed a fuzzy proportional integration (PI) controllerfor micro-hydro power plants to improve its performance. Salhiet al. [29] have used TakagiSugeno fuzzy system to model andcontrol a micro hyrdro power plant. To combine efciency andsimplicity of design, PI controllers are synthesized for eachconsidered operating point. A self-tuning fuzzy PI controller isproposed for frequency regulation when there are large loadvariations on micro-hydro power plants [30].

With the escalation in the computing power several complexalgorithms such as PSO, CLS, GA have become solvable today.Integrating fuzzy logic with these techniques have beenresearched in renewable energy systems. Moghaddam et al. [31]have presented an expert multi-objective Adaptive ModiedParticle Swarm Optimization (AMPSO) algorithm for optimizingthe operation of a micro-grid with renewable energy sources. Theoptimization process has been based on Chaotic Local Search (CLS)mechanism and a Fuzzy Self Adaptive (FSA) structure. The authorshave compared the above results with conventional PSO and GA[32]. Benachaiba et al. [33] have applied fuzzy logic techniquewithin micro-grid energy system. It is used to track the distur-bance of the grid and improve the quality of system. Fuzzy logictechnique is found to be very compatible even when used inconjunction with other complex algorithms for arriving at effectivesolutions.

Smart grid is an emerging topic of interest among bothresearchers and practioners. However a major concern in smartgrid implementation is the integration of renewable energysources for optimal distribution of electricity. Mohamed andMohammed [34] have proposed an algorithm for optimizing thedistribution system operation using a fuzzy based smart controllerin a smart grid considering cost and system stability. The algo-rithm aims at controlling the power available from differentsources while giving top priority to renewable energy sources.Three to six fuzzy sets have been used to map the input variableinto a fuzzy set.

Renewable energy unit commitment in a micro-grid formed bya mix of intelligent buildings of both ofce and residential naturehas been found [35]. The optimization problem has been solvedusing simulated annealing heuristic technique. Since there isalways ambiguity in predicting the climatic conditions, estimatingthe solar energy was found to be difcult using mathematical andregression models. A fuzzy model has been used in the constrain-ing condition. Fuzzy logic was used for modeling and estimatingthe solar energy using the sunshine availability, temperature,

location etc. for smart grid applications [36]. Rezvani et al. [37]have performed economic and environmental pareto optimalscheduling of a micro-grid for using renewable energy sources.The decision making process adopts fuzzy logic technique. Thescheduling of energy sources in a micro-grid comprising of micro-turbine, PV, fuel cell, battery units and wind turbine have beencarried out using fuzzy based optimization models with twoobjective functions of minimizing the total operation cost andemission. A fuzzy satisfying method is used for the decisionmaking process [38]. Normal boundary intersection technique isused to solve the multi-objective optimization and generate thePareto set. The above research indicates how fuzzy logic has beeneffectively deployed in optimization problems to arrive at optimaloutput either for constraining conditions or for optimal schedulingof renewable energy resource in grid optimization.

Fuzzy logic has also been tried with other advanced optimiza-tion techniques such as honey bee optimization for determiningthe best renewable energy resource for electricity generation.Niknam et al. [39] have considered photovoltaic, wind turbineand fuel cell based electrical generators to determine the sitingand sizing of renewable electricity generators using pareto basedHoney Bee Mating Optimization (HBMO) algorithm. The objectivesconsist of minimization of costs, emission and losses of distributedsystem and optimization of voltage prole. The comparison ofvarious methods indicate how inclusion of fuzzy logic improvesthe precision accuracy. The review clearly indicates how linguisticvalues are made measurable using fuzzy logic and also when usedalong with advanced forecasting/optimization/scheduling techni-ques, the methodology becomes robust and the decision outcomesare more accurate. The authors have presented Modied HoneyBee Mating Optimization (MHBMO) [40,41] to study the distribu-tion feeder conguration (DFR) considering renewable energysources such as photovoltaics, fuel cell and wind energy. A fuzzyclustering algorithm and fuzzy based decision maker is adopted toselect the optimal solution. The effectiveness was checked usingtwo standard distribution systems.

Earlier reviews indicated how fuzzy AHP has been used to ndrelative scores or ranking of resources. Fuzzy TOPSIS is yet anotherMCDM technique which has been used for ranking of renewableenergy supply systems under conicting scenarios. The techniquewas adopted for Turkey [42] to rank the resources considering theamount of energy produced, land use, operation and maintenancecost, installed capacity, efciency, investment cost, payback period,creation of jobs, CO2 emission. Twenty four criteria were initiallyidentied of which nine criteria only were selected for analysis. Itis suggested to run the model with all the criteria depending onthe region chosen for study.

Adaptive chaos clonal evolutionary programming (ACCEP) hasbeen used by Hong and Lin [43] for short-term active powerscheduling of a stand-alone wind and solar PV system. As fuzzylogic was found to be an highly appropriate tool in capturinguncertainties, fuzzy sets was used to model the uncertainties. Thefuel cost and CO2 emission that will result from such systems arealso analysed.

Clustering is used for grouping items of similar characteristics.Zare and Niknam [44] have developed a multi-objective problemconsidering conicting objectives; i.e. the electrical energy losses,the voltage deviations, the total electrical energy costs and thetotal emissions of renewable energy sources and substations. Afuzzy clustering technique has been used to group the enormousamount of data into groups for further analysis. This helps incontrolling the size of the repository.

The applications of soft computing techniques (namely fuzzylogic, neural network, simulated annealing, genetic algorithm etc.)in energy modeling have increased in recent years. Ishaque andSalam [45] have reviewed the research carried out for maximum


power point tracking (MPPT) for PV systems while Roy and Saha[46] have reviewed the research in soft computing techniques tooptimize the various parameters and the efciency of Savoniouswind rotors.

Fuzzy based hybrid models used in renewable energy systemsare categorized based on the techniques used and the areas ofresearch wherein they were adopted and presented in Table 1. Theapplications of fuzzy logic in solar and wind energy research arefound to be more than in other energy systems.

It is clear from the table that neuro-fuzzy and ANFIS modelswere predominantly used in the early years. The increase incomputing power and advancement of technology has resultedin the extensive use of MCDM techniques, especially in theemergence of integrated models such as fuzzy expert, fuzzy AHP,fuzzy ANP, fuzzy genetic, fuzzy DEA, neuro-fuzzy-DEA, fuzzyVIKOR, fuzzy TOPSIS, fuzzy PSO, fuzzy ant colony optimizationetc. Table 2 clearly highlights how fuzzy based integrated modelshave increased tremendously over the years in various applicationdomains. The table clearly highlights how the number of researchpapers that have been published using fuzzy based models inrenewable energy systems have increased several fold in therecent years.

The applications of fuzzy logic in renewable energy systemshave been reviewed and was found that fuzzy logic based AHP,ANP, DEA have been used for ranking of renewable energyresources, to nd the relative importance of the resources.Neuro-fuzzy, fuzzy genetic algorithm, fuzzy simulated annealinghave been used for optimization. However researchers are explor-ing newer and complex techniques in recent years to accommo-date heuristic based reasoning and obtain better results. This hasled to the emergence of techniques such as ant colony, particleswarm, honey bee optimization etc. When fuzzy logic is integratedto these techniques the results are found to be more precise andaccurate.

4.1. Solar

Mamlook et al. [137] have presented a fuzzy set methodologyfor comparing the benet to cost ratios of different solar systemsfor various applications in Jordan. Overall fuzzy weights is givenfor each solar system. GIS based spatial fuzzy multi-criteriaevaluation was carried out at Oman by Charabi and Gastli[119,138] to assess the land suitability for large PV farms imple-mentation. The electricity generation potential using solar PVtechnology on highly suitable lands have been estimated. SimilarlyGunderson et al. [139] have adopted graphical method based onfuzzy logic to determine the potential sites for PV power plants.Wu et al. [121] have used fuzzy measure to weigh the importantcriteria and linguistic Choquet operator to rank the various solarthermal power plant sites. Fuzzy densities have been obtained forcriteria and sub-criteria. The criteria used are energy factor,infrastructure factor, land factor, environmental and social factor.Each of these criteria has its own sub-criteria. Dynamic fuzzyreliability models were used to resolve the problems in dealingwith the interaction between the fuzzy stress process and thestochastic strength process in folded solar array system [140].Fuzzy logic is found to be an adequate tool to handle spatial datafrom GIS, simulation data, index data from reliability models toidentify potential sites for solar system installations.

Zhang et al. [141] have proposed an energy managementstrategy in buildings which uses PV. The energy management inbuildings using PV power becomes complex because of severalreasons such as intermittent supply, storage, power variation. Themodelaims to reduce the cost of energy bill and CO2 emissionusing fuzzy logic.

The rating scale incorporates the fuzzy ranges for variousparameters in survey based studies measuring consumer's prefer-ences. Zhai and Willaims [142] have developed a fuzzy logicinference model relating consumer perception variables to theprobability of purchase of energy from solar PV. The survey wasconducted among consumers in Arizona, US. Purchasing prob-ability distribution has been obtained among adopters and non-adopters using fuzzy logic model. Liu et al. [143] have evaluatedthe challenges for solar energy utilization in Taiwan and haveidentied strategies for energy development in the future usingfuzzy delphi method. Eight objectives and forty ve criteria havebeen studied to determine the strategies. The fuzzy ranges arethen converted to crisp accurate values using the fuzzy setmethodology. This conversion from fuzzy to crisp value helps inaccurately sieving through the decisions existing in the minds ofthe consumers.

Fuzzy logic based controllers have been compared with con-ventional PI controllers and in various combinations thereof formaximum power point tracking in solar PV systems. A fuzzy logicpower tracking controller has been developed for a standalonephotovoltaic energy conversion. Altas and Sharaf [144] state thatthe fuzzy logic design proposed can also be used for other powersystem applications such as voltage control, power system stabi-lizers and speed control application. Nafeh et al. [145] haveevaluated the controller performance for maximum power pointtracking using conventional PI controller and fuzzy logic controllerusing an experimental setup.

An online fuzzy optimization procedure [101] has been designedfor maximization of the efciency of a photovoltaic water pumpingsystem. The model in turn maximizes the drive speed and the waterdischarge rate of the centrifugal pump. Simulation studies indicatethe proposed fuzzy controller provides a highly accurate onlinetracking system for nding the efciency of the PV pumpingsystem. Alata et al. [52] has utilized Sugeno fuzzy inference systemfor modeling and controller design in sun tracking system. Asubtractive clustering algorithm is used with least square estima-tion (LSE) to generate fuzzy rules that are again tuned by adaptiveneuro-fuzzy inference system (ANFIS). The Simulink and VirtualReality toolboxes of MATLAB are used in the simulation studies.Chaabene et al. [146] have proposed a new switching approachusing fuzzy logic for obtaining optimum output when connectingdomestic appliances to either the electrical grid or a photovoltaicpanel. A fuzzy management algorithm has been proposed in thispaper. In addition to conventional fuzzy, neuro-fuzzy controllers,fuzzy cognitive, online fuzzy dynamic search are being researchedinto. Karlis et al. [147] present a Maximum Power Point TrackingMethod (MPPT) using fuzzy cognitive network. The authors statethat power point trackers play an important role in photovoltaic(PV) power systems because they maximize the power output froma PV system for a given set of conditions such as changing insolationand temperature, and therefore maximize the array efciency. Theauthors claim that the numerical results show the effectiveness ofthe proposed algorithm. An on-line fuzzy logic-based dynamicsearch, detection and tracking controller for a standalone solar PVis developed to ensure maximum power point (MPP) operation forvariations in solar insolation, ambient temperature and electric load[148]. It is found that any system incorporating fuzzy approachgives better results in terms of tracking efciency.

Larbes et al. [149] have presented an intelligent fuzzy logiccontrol method for maximum power point tracking (MPPT) of aphotovoltaic system under variable temperature and irradianceconditions. The fuzzy control is made intelligent using geneticalgorithms for optimization. The optimized fuzzy logic MPPT con-troller is evaluated under varying conditions and was found to bemore robust giving better performances as compared to conven-tional perturbation and observation (P&O) MPPT algorithms. The


Table 2Applications of fuzzy logic in renewable energy modeling over the years.

Year Techniques Area of research Researchers

1995 Fuzzy expert Building design Dounis et al. [16]1999 Fuzzy GA Solar Luk et al. [110]2000 Neuro-fuzzy, ANFIS Wind Sfetsos [82]2001 Neuro-fuzzy, ANFIS Renewables Mamlook et al. [9]

Fuzzy MCDM Renewables Agrawal and Singh [10]2002 Neuro-fuzzy, ANFIS Bio-energy Jurado and Saenz [86], Jurado et al. [87]2003 Fuzzy clustering Solar Gomez and Casanovas [95]2004 Fuzzy optimization Solar Benlarbi et al. [101]2005 Neuro-fuzzy, ANFIS Solar Alata et al. [52]

Zarzalejo et al. [66]2006 Fuzzy expert Renewables Kaminaris et al. [7]2007 Fuzzy clustering Bio-energy Ayoub et al. [100]2008 Neuro-fuzzy, ANFIS Solar Cao and Lin [54], Chaabene and Ammar [55], Mellit et al. [61]

Fuzzy AHP, ANP Space heating Jaber et al. [17]Fuzzy clustering Wind Calderaro et al. [97], Ustuntas and Sahin [99]Fuzzy MCDM Solar Salah et al. [120]Fuzzy PSO Wind Wang and Singh [134]

2009 Neuro-fuzzy, ANFIS Solar Syafaruddin et al. [65]Wind Monfared et al. [76]

Fuzzy AHP, ANP Renewables Kahraman et al. [20]Bio-energy Chang et al. [93]

Neuro-fuzzy expert Bio-energy Romeo and Gareta [118]Fuzzy MCDM Bio-energy Bitar et al. [122]

2010 Fuzzy regression Solar Martin et al. [47]Neuro-fuzzy, ANFIS Solar Chaouachi et al. [56]

Wind Haque et al. [69], Hong et al. [70], Lin et al. [72]Fuzzy AHP, ANP Renewables Heo et al. [22]Fuzzy AHP, ANP Hybrid Chen et al. [94]Fuzzy GA Hybrid Ansari and Velusami [112,113], Kalantar and Mousavi [114]Fuzzy TOPSIS, VIKOR Renewables Kaya and Kahraman [21]

Solar Cavallaro [126]Fuzzy PSO Solar Welch and Venayagamoorthy [129]

2011 Fuzzy regression Wind Kaneko et al. [50]Neuro-fuzzy, ANFIS Solar Lee et al. [60], Mellit and Kalogirou [62], Salah and Ouali [63]Neuro-fuzzy, ANFIS Wind De Giorgi et al. [68], Lin et al. [73], Mohandes et al. [75], Pousinho et al. [81], Yang et al. [85]Neuro-fuzzy, ANFIS Hybrid Rajkumar et al. [88]Fuzzy AHP, ANP Solar Lee et al. [60]Fuzzy clustering Fuel cell/solar/wind Niknam et al. [40]Fuzzy optimization Wind Liang et al. [102]Neuro-Fuzzy DEA Solar Azadeh et al. [107]Fuzzy GA Wind Gonzlez de la Rosa et al. [111]Fuzzy MCDM Solar Charabi an Gastli [119]Fuzzy PSO Micro-grid Moghaddam et al. [31]Fuzzy PSO Wind Liang et al. [133], Lin et al. [73], Pousinho et al. [81]Fuzzy honey bee optimization Renewables Niknam et al. [39,40]

2012 Neuro-fuzzy, ANFIS Solar Landeras et al. [59]Fuzzy AHP, ANP Wind Lee et al. [89], Liu et al. [90]Fuzzy clustering Fuel cell/solar/wind Niknam et al. [41]Fuzzy clustering Wind Azizipanah-Abarghooee et al. [96]Fuzzy optimization Bio-energy Tan et al. [105]

Hybrid Malekpour et al. [106]Neuro-Fuzzy GA Wind Kasiri et al. [116]Fuzzy MCDM Wind Lee et al. [89]Fuzzy PSO Micro-grid Moghaddam et al. [32]Fuzzy honey bee optimization Renewable Niknam et al. [41]

2013 Neuro-fuzzy, ANFIS Solar Chen et al. [57]Wind Liu et al. [74], Petkovi et al. [78]

Fuzzy clustering Renewables Zare and Niknam [44]Wind Ugranli and Karatepe [98]

Fuzzy MCDM Bio-energy Ren et al. [123]Fuzzy TOPSIS, VIKOR, Solar Ahmadi et al. [124,125]

Hybrid Perera et al. [127]Fuzzy PSO Wind Aghaei et al. [131], Bahmani-Firouzi et al. [132]

2014 Fuzzy regression Solar Ramedani et al. [48]Wind An et al. [49], Wang and Xiong [51]

Neuro-fuzzy, ANFIS Solar Arulmurugan and Suthanthiravanitha [53], Kharb et al. [58]Wind Capovilla [67], Jafarian and Ranjbar [71], Petkovic et al. [79,80], Shamshirband et al. [83,84]

Fuzzy AHP, ANP Renewables Ren and Sovacool [24], Tarsri and Susilawati [23]Wind Yeh and Huang [92]

Fuzzy optimization Renewables Ho et al. [12], Li et al. [13], Vahidinasab [14]Bio-energy Balaman and Selim [103]

Neuro, Fuzzy DEA Wind Azadeh et al. [108]Fuzzy GA Solar Kisi [109]Neuro-Fuzzy GA Solar Chekired et al. [115]Fuzzy expert Renewables Kyriakarakos et al. [8]


research by Lalouni et al. [150] have proposed a maximum powerpoint tracker (MPPT) method, based on fuzzy logic controller (FLC),applied to a stand-alone photovoltaic system which is run onbattery storage. The method was found to give good results whenthe PV system was operating at maximum power regardless of theweather variations with good tracking mechanism. The aboveresearch conrms the usefulness and effectiveness of fuzzy basedcontrollers in maximum power point tracking. The review alsohighlights how when fuzzy logic is integrated with other techniquessuch as genetic algorithm better performances are obtained.

DSouza et al. [151] have discussed the use of fuzzy logic andnon-switching zone schemes for implementing variable sizeperturbations for improved transient and steady-state responsesin the tracking for solar PV system. A fuzzy P&O MPPT algorithmyielded improved outcomes. A fuzzy logic-incremental conduc-tance (FL-IC) MPPT scheme for PV array is proposed by Niapouret al. [152]. The proposed system was simulated for variousconditions and the effectiveness of FL-IC MPPT was validated.The review thus indicates that whenever fuzzy logic is integratedto conventional methods, the model gave better results.

Fuzzy controllers are synchronised with neural networks to checkits performance against the conventional MPPT in grid connectedsystems. Chaouachi et al. [56] have presented a methodology forMaximum Power Point Tracking (MPPT) of a grid-connected 20 kWphotovoltaic (PV) system using neuro-fuzzy network. The neuro-fuzzy network is composed of a fuzzy rule-based classier and threemulti-layered feed forwarded Articial Neural Networks (ANN). Thesimulation results proved that the proposed MPPT method resultedin higher efciency as compared to a conventional neural networkand Perturb and Observe (P&O) algorithm. Salah and Ouali [63]compare two types of controllers fuzzy logic and neural networkcontrollers, for maximum power point tracking. The authors haveconcluded from the simulation and experimental results that fuzzylogic controller gave better results. As already found the results againconrm that when fuzzy logic is integrated with neural network theperformance of the system is better.

Mazouz and Midoun [153] have proposed a technique forderiving maximum power point (MPP) in a photovoltaic arrayusing fuzzy logic. The system consists of a photovoltaic array and aDC engine connected to a centrifugal pump, through a DCconverter. The electricity is used for solar pumping. Their experi-mental results validate the theoretical model and highlight theutility of the fuzzy controller for system optimization.

The functioning of fuzzy logic controller is analysed amongdifferent types of PV systems and modules [154157]. Messai et al.[158] in their paper describe the hardware implementation of a fuzzylogic controller on a Xilinx recongurable Field-Programmable GateArray (FPGA). The FLC is aimed to derive maximum power fromphotovoltaic modules. Messai et al. [159] have adopted Geneticalgorithm optimized fuzzy logic controller for maximum powerpoint tracking on a Xilinx recongurable Field-Programmable GateArray (FPGA) chip for a standalone PV system.

Gheibi et al. [160] have proposed a MPPT method for obtainingmaximum power from solar PV cells based on type-2 fuzzy logic

control. Type-1 FLC was being conventionally used to modelcomplex nonlinear systems. In this paper the authors have triedmodeling with type-2 FLC to minimize uncertainties. They foundType-2 FLC performed better when noise was present. Howeverbecause of the complex analytics involved the processing tookmore time than type-1 FLC. Ramaprabha et al. [161] have pre-sented a modied Fibonacci search method with fuzzy controllerfor MPPT of a partially shaded solar PV system. Larabi et al. [162]have designed a fuzzy logic based (DC/DC) converter for MPPT ofsolar PV. To adapt the rotor time constant a fuzzy tuningmechanism is designed based on the reactive power of themachine. Subiyanto et al. [163] have used Hopeld neural network(HNN) optimized fuzzy logic controller (FLC) for MPPT of PVenergy harvesting system. The review clearly illustrates theusefulness of fuzzy logic controllers in all types of PV systemsfor MPPT. Also fuzzy logic integrated approaches were found to beeven more versatile.

Eltawil and Zhao [164] have compared the major characteristicssuch as PV array dependent, true MPPT, analog or digital, periodictuning, convergence speed, implementation complexity etc. forvarious MPPT techniques in PV applications and have concludedthat intelligent methods (namely fuzzy logic with heuristic reason-ing) are efcient in obtaining maximum energy from varyingtemperature and irradiance conditions. They have presented thetype of MPPT technique to be adopted under certain conditions.Altin and Ozdemir [165] have proposed a system which consists ofthree-level neutral point clamped inverter, output lter, linefrequency transformer, PI current regulator and fuzzy logic basedmaximum power point tracking algorithm. The simulation andexperimental results show that even when the atmosphericconditions vary the system is capable of tracking the maximumpower point of PV system.

Fuzzy logic was explored with articial intelligence algorithmsand the results were analysed. Adly and Besheer [166] haveanalysed the problems of MPPT for stand-alone solar PV systems.A meta-heuristics search algorithm using ant based controlleroptimization is proposed for maximum energy transfer aftercomparing with PI and fuzzy control mechanisms. Rajkumaret al. [167] have carried out simulation and experimental investi-gation of space vector pulse width modulation (SVPWM) in PVsystems. The MPPT algorithm is solved by fuzzy logic controller.The proposed system was designed and testing using MATLAB/Simulink. Such systems which mime human reasoning were foundto produce higher precision accuracy.

Several research has compared the use of fuzzy logic controllerwith various types of controllers for MPPT in PV systems. Theresults clearly indicate the superiority of fuzzy based controllers.Dounis et al. [168] have proposed a methodology of designing aMPPT controller for photovoltaic systems (PV) using a Fuzzy GainScheduling (FGS) of Proportional-Integral-Derivative (PID) typecontroller (FGS-PID) with adaptation of scaling factors (SF) forthe input signals of FGS. Rebhi et al. [169] have compared theelectrical performances of DC-DC converter for MPPT of PVsystems. The comparison is made between Perturb and Observe,

Table 2 (continued )

Year Techniques Area of research Researchers

Wind Zhao et al. [117]Fuzzy MCDM Solar Wu et al. [121]

2015 Neuro-fuzzy, ANFIS Wind Osorio et al. [77]Fuzzy AHP, ANP Wind Shaee [91]Fuzzy optimization Bio-energy Ng et al. [104]Fuzzy TOPSIS, VIKOR Renewables Sengul et al. [42]Fuzzy PSO Solar Sangawong and Ngamroo [128], Zeng et al. [130]Fuzzy PSO, QPSO, Cuckoo optimization Hybrid Berrazouane and Mohammedi [135] Bigdeli [136]


PID and fuzzy logic controller (FLC) to loading a storage battery.Fuzzy MPPT control is found to provide quick stability and higherperformance precision. The simulation study indicate fuzzy PIDcontrol is the best method.

Lalouni and Rekioua [170] have presented MPPT algorithms forgrid connected PV system. To determine the optimal energy that canbe drawn under varying solar irradiance and temperature conditions,Perturb and Observe (P&O), Incremental Conductance (Inc Cond)techniques and fuzzy logic controller (FLC) are adopted and theresults compared. Houssamo et al. [171] have analysed the impact ofMPPT methods for PV systems: Perturb and Observe (P&O) andIncremental Conductance, as tracking step constant, and improvedP&O and Fuzzy Logic based MPPT, as variable tracking step. TheMPPT algorithms for PV power system have been compared experi-mentally. Improved P&O operates better in terms of energy efciencyand cost effectiveness. Ramasamy and Thangavel [172] have pro-posed a PV based dynamic voltage restorer (PV-DVR). The use of lowstep-up DC-DC converter with fuzzy based Perturb and Observe(P&O) MPPT algorithm helps in mitigating the voltage sags andswells. Punitha et al. [173] have presented a neural network basedmodied incremental conductance algorithm for MPPT in PV systemunder partial shading conditions. The results are compared withPerturb and Observe, and Fuzzy based Modied Hill Climbingalgorithms. The PV system along with the proposed MPPT algorithmwas simulated using MATLB/Simulink. All the above research indi-cates the primacy, the exibility, the integratability of fuzzy logic.This has been established using theoretical models, simulationmodels as well as in experimental research.

The various applications of MPPT based articial intelligencetechniques such as neural networks, fuzzy logic, genetic algorithm,particle swarm optimization, ant colony optimization etc. forphotovoltaic systems over the years have been reviewed [174].The review clearly brings forth the emergence of fuzzy logic andfuzzy logic integrated models as a robust tool for renewableenergy modeling. Rajesh and Mabel [175] propose a multi-fuzzylogic based maximum power point tracking controller to track themaximum power from PV system. A fuzzy PI controller is used aspower lter for grid connected photovoltaic system [176]. Theshunt active lter (SAF) topology consists of ve level cascadedmultilevel inverter with fuzzy PI controller to regulate the DC sidevoltage. The control system operation was simulated usingMATLAB/Simulink. Experimental results conrm the efciency ofthis method. Visek et al. [177] have used fuzzy PID control withTRNSYS and MATLAB simulation for improving energy efciency ina solar cooling system. Yahyaoui et al. [178] have proposed a fuzzylogic methodology for off grid installation with PV panels. Detailedtests were carried out using the data on irradiation, temperature,power consumption etc. measured in a household. The results ofthe study indicate battery protection and power supply stability isachieved by the system. The results from the simulation [179]show the effectiveness of advanced fuzzy MPPT controller duringboth steady-state and varying weather conditions for a standalonePV system. Several softwares have been developed integratingfuzzy logic with AI techniques in their toolbox. The reviewindicates fuzzy based controllers for MPPT helps in trackingmaximum power.

Researchers are experimenting with different techniques inisolation and in combination and comparing it with fuzzy andfuzzy integrated systems for MPPT in PV modules. Arulmuruganand Suthanthiravanitha [53] have proposed an optimized fuzzylogic controller coupled with Hopeld neural network maximumtracking technique to extract maximum amount of power from PVmodules. Simulation studies were carried out using MATLAB/Simulink. The results validate the effectiveness of the HopeldNN using the optimized fuzzy system. Kharb et al. [58] haveproposed an adaptive neuro-fuzzy inference system (ANFIS) based

on maximum power point tracking controller (MPPT) to extractmaximum power. Chekired et al. [115] have compared the variousintelligent methods such as neural networks (NN), fuzzy logic (FL),genetic algorithm (GA) and hybrid systems (e.g. neuro-fuzzy orANFIS and fuzzy logic optimized by genetic algorithm) for max-imum power point tracking (MPPT) of PV systems. Simulation wasperformed using ModelSim and ISE. It was found that fuzzy logicoptimized by genetic algorithm was the best.

Zeng et al. [130] have used fuzzy approach to deal withuncertainties in the levelized cost of electricity from solar PV.FMEA is used to calculate the RPN for each failure in powergeneration. A multiple objective particle swarm optimization(PSO) method was then deployed to deal with the uncertaintiesin levelized cost of electricity and the risk levels of failure modes.This was then used to evaluate the investor owned solar PVprojects. Sangawong and Ngamroo [128] have highlighted howthe frequency can be stabilized in multi area grid-connected largePV farms using a PSO based optimal Sugeno fuzzy logic controller.The above research has validated the claim that any heuristicbased (namely PSO, GA, ANN) fuzzy approach yielded betterperformances.

Fuzzy logic has also been used to predict solar radiation. Sen[180] have developed a fuzzy algorithm using linguistic variablesfor estimating solar irradiation. The fuzzy approach is applied atthree sites to measure the solar irradiation in Turkey. Sfetsos andCoonick [64] have adopted univariate and multivariate techniques,linear, feed-forward, recurrent Elman and Radial Basis neuralnetworks and adaptive neuro-fuzzy inference scheme to forecasthourly solar radiation. Gomez and Casanovas [95] have carried outfuzzy clustering of solar irradiance on inclined surfaces. The use offuzzy optimizes the number and denition of the sky categories.Santigosa et al. [181] have used three models based on multi-variate linear regression, non-linear regression and fuzzy inferencesystem to evaluate the daily ultraviolet radiation using data fromthree Spanish locations. It was found fuzzy inference system gavegood results. The studies have indicated that fuzzy inferencinghelped in better prediction capability.

Fuzzy logic and neural networks have been used to measureglobal irradiance in Spain. The authors [66] state that this model isfound to give better results as compared to multivariate regressionbased model. A diagonal recurrent wavelet neural network(DRWNN) is developed by Cao and Lin [54] using recurrent neuralnetwork (RNN) and wavelet neural network (WNN). Fuzzy logic isused for mapping nonlinear functions to forecast hourly and dailyglobal solar irradiance. The magnitude of solar radiation is essen-tial to determine the size of solar PV systems. Mellit et al. [61] haveadopted neuro-fuzzy inference system (ANFIS) for estimating thesequences of mean monthly clearness index and daily solarradiation in remote areas. The system can also be used forestimating other meteorological parameters such as temperature,humidity and wind speed. Thus fuzzy logic has been used with NNto improve the predictive ability of the models.

Traditional forecasting tools such as regression, time series,winter's method and ARMA were being used to predict solarirradiance. However it was found that when fuzzy, neuro-fuzzywas integrated to these traditional models, the algorithms thoughcomplex yielded higher accuracy. A neuro-fuzzy model has beenused by Chaabene and Ammar [55] to dynamically forecastirradiance and temperature for solar energy systems. Capture ofdynamic behavior is ensured by the Auto-Regressive MovingAverage (ARMA) model associated with Kalman lter. Paulescuet al. [182] have used fuzzy logic algorithms for evaluating atmo-spheric transmittances for use of solar energy. Martin et al. [47]have predicted global solar irradiance for applying in solar thermalpower plants using autoregressive, neural networks and fuzzylogic models.


Since the meteorological data has various seasonal, monthlyand daily changes, ARMA model was adopted for prediction. Mellitand Kalogirou [62] have developed a ANFIS based model formodeling and simulating the photovoltaic power supply system.Measured climate data and electrical data had been used inmodeling the system. The authors state that when they comparedit with NN and ANFIS models gave better results.

Further advanced algorithms were tested for solar radiationpredictions. Landeras et al. [59] have estimated the daily incomingsolar radiation in the Basque Country (Northern Spain) using GeneExpression Programming, neuro-fuzzy and neural network. Acomparison has been made among the techniques and it wasfound GEP. GEP approach when used to model global solarradiation using daily atmospheric variables resulted in higherpredictive capability. Chen et al. [57] have forecasted solar radia-tion using fuzzy logic and neural network under varying weatherconditions. The model is validated by testing the model using datafrom four different scenarios. The review indicates that fuzzy logicnot only helps in capturing the fuzziness in the environment butalso enhances the accuracy of prediction when used with otheralgorithms.

Ramedani et al. [48] have predicted the solar radiation usingfuzzy linear regression and support vector regression in Iran andfound SVM gave better results. Feature space, radial basis functionkernel, support vector regression were all adapted in the model toenhance the predictive power. Kisi [109] has determined the solarradiation in Turkey using fuzzy genetic (FG) approach. A compar-ison is made with the results obtained from ANN and ANFISmodels and it was found the results from FG model had higherpredictive capacity. The review indicates that whether it is fuzzyGA or fuzzy SVM or any fuzzy based model helps in effectivemodeling of any system.

In addition to solar PV systems, fuzzy logic has been used in avariety of applications. Luk et al. [110] have proposed a geneticalgorithm based fuzzy logic control of a solar power plant usingdistributed collector elds in Almerfa, Spain. Kaushika and Reddy[183] have investigated the performance of a low cost solarparaboloidal dish steam generating system. Semi-cavity and mod-ied cavity receivers which are thermally optimized, with thefuzzy focal image have been investigated. Kumar and Reddy [184]have investigated using a 2-D model the natural convention heatloss in modied cavity receiver for fuzzy focal solar dish concen-trator. Kumar and Reddy [185] have studied the natural convectiveheat loss from three types of receivers for a fuzzy focal solar dishconcentrator. Reddy and Kumar [186] have proposed a 3-Dnumerical model to estimate the natural convection heat lossfrom modied cavity receiver (WOI) of fuzzy focal solar dishconcentrator. Fuzziness has been used in the focal image in solardish concentrators and the theoretical investigations revealed thatin such systems the heat loss was better optimized using fuzzyapproach.

A multi-objective thermodynamic based optimization from theSolar Dish-Stirling engine using evolutionary algorithms have beenproposed by Ahmadi et al. [124,125]. Three objective functionsnamely maximization of output power and overall thermal ef-ciency and minimization of the rate of entropy generation of theStirling engine have been considered. The optimal solution isobtained using various decision making techniques such as fuzzyBellmanZadeh, LINMAP and TOPSIS methods. An optimizationmodel for extension of the operation time of the water pumpusing fuzzy rules has been presented for a photovoltaic waterpumping system [187,188].

Fuzzy based approach have been investigated in various dis-ciplines such as solar air conditioning, solar water pump etc. Sozenet al. [189] present performance prediction of a solar drivenejector-absorption cycle using fuzzy logic controller. Fuzzy logic's

linguistic terms were used to dene the operational characteristicsof the solar driven ejector absorption refrigeration system (EARS).Lygouras et al. [190] have presented a fuzzy logic controllerimplementation for a solar air conditioning system. Lygouraset al. [191] have presented a Two-Input/Two-Output (TITO) vari-able structure fuzzy-logic controller for a solar-powered air-con-ditioning system. A coupling fuzzy controller is incorporated intothe traditional fuzzy controller. This mixed fuzzy controller isfound to improve the control performance.

Kishor et al. [192] have used three inputs: (i) inlet watertemperature, (ii) ambient temperature (iii) solar irradiance; inthe three models each with one, two, three inputs and foundhow a three input fuzzy model improves the accuracy in theprediction of performance of a thermosyhon solar water heatingsystem. Salah et al. [120] have proposed a multi-criteria fuzzyalgorithm which makes decision for energy management using adomestic photovoltaic panel. The behavior of a three phase gridconnected photovoltaic system using fuzzy logic controller isinvestigated by Hamzaoui et al. [193]. The simulation results werefound to be consistent with theoretical analysis and conrmed theexcellent performance of the proposed scheme. Chekired et al.[194] using fuzzy logic has optimized the storage and distributionof energy of a standalone photovoltaic system.

Syafaruddin et al. [65] have used real-time simulation techni-que of PV generation system by using dSPACE real time interfacesystem. ANN and fuzzy logic controller are adopted and the systemused polar information to verify the availability and stability of thesystem. The simulation was conducted using MATLAB/Simulink.Welch and Venayagamoorthy [129] have adopted particle swarmoptimization algorithm to optimize the membership function ofthe fuzzy logic controller for a grid independent photovoltaicsystem. Cavallaro [126] has proposed fuzzy multi-criteria TOPSISapproach for assessing thermal-energy storage using differentheat transfer uids in concentrated solar power (CSP) systems.In all these research, the authors have claimed how fuzzy basedalgorithm is a technique which aids in accurate modeling of anysystem.

Saglam et al. [195] have converted the DC electrical energy fromsolar photovoltaic panels to AC electrical energy using fuzzy logicbased controller. Ammar et al. [196] have used neuro-fuzzy methodto forecast the energy need for the subsequent day using theprevious few day's generation from a household photovoltaic panel.

Fuzzy ANP, fuzzy DEA are techniques which help to system-atically optimise and arrive at relative scores among severalcompeting criteria. Lee et al. [60] have developed a conceptualmodel using fuzzy analytic network process with interpretivestructural modeling and benets, opportunities, costs and risksto analyze strategic products for photovoltaic silicon thin-lmsolar cell power industry. The authors claim their results are ableto realistically match with the future solar PV plans. Azadeh et al.[107] have used a exible neuro-fuzzy approach for locationoptimization of solar plants. The exible approach is composedof articial neural network (ANN) and fuzzy data envelopmentanalysis (FDEA). FDEA is found to be a better model in thepresence of uncertainty and noise in the dataset.

Table 3 gives a comprehensive picture of the various fuzzybased models used in solar energy applications.

The review indicates that several researchers have worked onfuzzy logic controller for MPPT in solar PV systems. Someresearchers have used fuzzy logic modeling for solar farm siteselection. The review clearly highlights how fuzzy based modelsare better in terms of realistically assessing the site, resources, oroptimise the functioning, or predict the radiation, or evaluate thealternatives, or for maximum power point tracking. The reviewindicates the varied elds (solar PV, solar pumping, solar desalina-tion, solar concentrators, solar site selection to name a few) in


which researchers have experimented with. The use of fuzzy basedsystems will improve the functioning and performance of anysystem. It is for this reason, several researchers have startedexperimenting with fuzzy integrated systems either in the formof controllers or with techniques. There is still wide scope forusing fuzzy integrated systems such as neuro-fuzzy, fuzzy AHP,fuzzy ANP, fuzzy DEA, fuzzy GA, fuzzy PSO in several solar energyapplications. Also the computing power has increased exponen-tially in recent years which give a greater impetus to use thesetechniques for effective energy modeling.

4.2. Wind

Fuzzy approach has been applied in wind energy for siteidentication, economic analysis, capturing uncertainties, windprediction, wind energy assessment etc. The probable sites wherewind energy conversion systems can be located have beenidentied using fuzzy based risk analysis [197]. The variables usedin the analysis are wind speed and its frequency of occurrence. Asalready noted Charabi and Gastli [119,138] had used spatial datawith fuzzy logic to identify solar PV farm site at Oman while Aydinet al. [198] have used GIS data and fuzzy sets to determine the sitefor installation of wind energy systems in Western Turkey.

Fuzzy set theory has been used to combine the economic andinvestment options. Skikos and Machias [199] have developed afuzzy based economic criterion, for evaluation of wind energyconversion systems (WECS). This method can be used for devel-oping a Decision Support System. A fuzzy controller has beenadopted [200] to mitigate the response of a structure subjected toenvironmental excitations (wind). The quality of the fuzzy con-troller with respect to parameter evaluation is conducted usingFORM\Sorm probability estimates of performance. The impact ofthe uncertainties in wind generation and its penetration areanalysed using fuzzy sets by Mangueira et al. [201]. The approachis based on a multi-linearized fuzzy power ow. Fuzzy approachwas found to effectively analyse the uncertainties present in windenergy.

Fuzzy modeling has also been used for estimation and optimi-zation of wind energy. Jafarian and Ranjbar [71] have adoptedfuzzy modeling techniques and articial neural networks toestimate annual energy output of a wind turbine. The accuracyof this method when compared with conventional methods wasfound to be higher. Siahkali and Vakilian [202] have developedfuzzy optimization method for electricity generation schedulingfrom large scale wind farms. The problemwas rst formulated as acrisp problem. Then using fuzzy membership function a fuzzyoptimization problem. This was then converted to a crisp

optimization problem using GAMS software as a mixed integernon-linear programming problem. The proposed approach wastested and the robustness of the model was established. In bothinstances fuzzy approach gave good results.

To capture the thought pattern among individuals in analysisand decision making fuzzy approach was adopted. Zhao et al. [117]have carried out a fuzzy cognitive mapping methodology toestablish a exible operating mechanism for wind power industry.The model is based on questionnaire surveys and expert inter-views. A fuzzy ANP model based on Chang's extent analysis isdeveloped by Shaee [91] to mitigate the risks associated withoffshore wind farms consisting of 30 wind turbines of 2 MW. Yehand Huang [92] has identied the factors that determine windfarm location using GQM, DEMATAL and ANP. It is found thatsafety and quality, environment and ecology are the two majorfactors. The results from the review revealed how fuzzy approachcan clearly capture the heuristic reasoning among individuals.

Liu et al. [90] have used fuzzy AHP for ranking the differenttypes of integration schemes among wind power projects. Atolerance matrix is introduced in the fuzzy AHP and then thejudgement matrix is obtained which helps in nding the relativeimportance of each scheme. Lee et al. [89] have evaluated usingmulti-criteria decision making technique the criteria for installa-tion of wind turbines in wind farms in Taiwan. The techniquesused are interpretive structural modeling (ISM) and fuzzy analyticnetwork process (FANP). The increased integration of wind powerinto the electric grid poses new challenges due to its intermittencyand volatility. The model using FANP helps in selecting suitableturbines for a wind farm. Pousinho et al. [81] have proposed anovel hybrid approach for Portugal, combining particle swarmoptimization and ANFIS, for short-term wind power prediction inPortugal. Yang et al. [85] have used wind shear coefcient andANFIS and found using MAE and RSME, that the accuracy of theresults was higher when ANFIS was used. The model was used tointerpolate the missing and invalid wind data in North China. Asalready seen in solar systems, fuzzy based models are used fornding the relative importance among alternatives (using fuzzyAHP, fuzzy ANP), prediction (ANFIS) and optimization (ANFIS andPSO) of wind energy systems.

Gonzlez de la Rosa et al. [111] state that local wind climate isusually measured using regional wind climate modulated by localtopography effects, roughness and obstacles in the surroundingarea. The authors in their paper have used fuzzy logic and geneticalgorithm based method to generate the local wind conditions andto optimise the wind by creating terrains. The genetic fuzzy learningmimes human intelligence and is found to provide the best solutionaccording to the variables and conditions imposed.

Table 3Applications of fuzzy logic models in solar energy.

Techniques Researchers

Fuzzy regression Martin et al. [47], Ramedani et al. [48]Neuro-fuzzy,ANFIS

Alata et al. [52], Arulmurugan and Suthanthiravanitha [53], Cao and Lin [54], Chaabene and Ammar [55], Chaouachi et al. [56], Chen et al. [57], Kharbet al. [58], Landeras et al. [59], Lee et al. [60], Mellit et al. [61], Mellit and Kalogirou [62], Salah and Ouali [63], Sfetsos and Coonick [64], Syafaruddinet al. [65], Zarzalejo et al. [66]

Fuzzy AHP, ANP Lee et al. [60]Fuzzy clustering Gomez and Casanovas [95]Fuzzyoptimization

Benlarbi et al. [101]

Neuro-Fuzzy DEA Azadeh et al. [107]Fuzzy GA Kisi [109], Luk et al. [110]Neuro-Fuzzy GA Chekired et al. [115]Fuzzy MCDM Charabi and Gastli [119], Salah et al. [120], Wu et al. [121]Fuzzy TOPSIS,VIKOR

Ahmadi et al. [124,125], Cavallaro [126]

Fuzzy PSO Sangawong and Ngamroo [128], Welch and Venayagamoorthy [129], Zeng et al. [130]


It was seen from the review of literature that several researchhas been carried out since the early 90s on fuzzy logic controllersin power generation. It was established that fuzzy controllers gavebetter performance and hence researchers started exploring it inrenewable energy systems. The review here presents the use offuzzy logic controllers in wind energy systems. Mohamed et al.[203] have adopted fuzzy logic control technique for tracking windenergy and sending this power to a wind energy conversionsystem having an asynchronous (DCACDC) link. Dadone andDambrosio [204] have developed an adaptive fuzzy logic controltechnique for a wind turbine-generator system. A numericallysimulated wind power plant is considered. The wind power plantis composed of a three blade horizontal axis wind turbineconnected to a three phase synchronous generator. Velusami andSingaravelu [205] have used graph theory and fuzzy logic basedsteady state analysis of wind driven single phase single windingself-excited induction generator. Li et al. [206] have evaluated thestability of micro-grid operation and discussed the control tech-niques of combining a micro-turbine with the fuel cell andelectrolyzer hybrid system using a fuzzy PI controller. The pro-posed control and monitoring system acts as a power qualitycontrol system. It improves the frequency uctuation caused byrandom power uctuations on the generation side and in theinterconnected microgrid power system. Besheer et al. [207] havedeveloped the design of fuzzy state feedback controller that notonly stabilize the fuzzy model but also the transient behavior inthe wind energy conversion system. Stability conditions areexpressed as linear matrix inequalities and solved using convexoptimization techniques. Li et al. [208] have proposed a directcurrent based d-q vector control technique by integrating fuzzy,adaptive and traditional PID control design for direct driven PMSGwind turbines. The model not only helped in achieving the desiredPMSG control objectives but also improved the performance of theoverall system.

Kamal et al. [209] have developed maximum wind powerextraction algorithms for variable speed wind turbines in hybridwinddiesel storage system (HWDSS). The proposed algorithmutilizes TakagiSugeno (TS) fuzzy controller. Wind powersmoothing has also been done using fuzzy logic pitch controllerand energy capacitor system for improving the micro-grid perfor-mance [210]. Comparison is made with conventional PI pitchcontroller to evaluate the effectiveness of the two controllersusing MATLAB/Simulink. Kamel et al. [211] have adopted fuzzylogic pitch angle controller for enhancing the micro-grid perfor-mance during islanding mode using storage batteries. The perfor-mance between the fuzzy controller and the conventional PIcontroller is compared.

In addition to the conventional fuzzy controllers, online fuzzyneural network controllers are proposed and analysed for windenergy conversion systems. Lin et al. [72] have presented an onlinefuzzy neural network controller for output maximization in a windenergy conversion system. The sliding mode observer has beenused. The system delivered maximum power with light weight,high efciency and high reliability. The design of fuzzy slidingmode loss minimization control for optimizing the speed of apermanent magnet synchronous generator (PMSG) in a windgeneration system is carried out by Lin et al. [212]. Further thefuzzy inference mechanism with center adaptation is used todetermine the optimal bound of uncertainties. The authors [73]have also presented the design of an on-line training recurrentfuzzy neural network (RFNN) controller for wind generationsystem with a high-performance model reference adaptive system(MRAS) observer for the sensorless control of an induction gen-erator (IG). Particle swarm optimization (MPSO) is used to adaptthe learning rates in the back-propagation process of the RFNNand to improve the learning capability. It is found that when fuzzy

integrated techniques are adopted, models gave better predictivecapability. This further justies the inferences also drawn inSection 4.1 for solar energy systems.

Calderaro et al. [213] have compared two voltage controlmethods one based on sensitivity analysis and the second basedon fuzzy control system for wind power distributed generators. Itwas found that fuzzy method was more efcient than thesensitivity method. Chowdhury et al. [214] have proposed smooth-ing wind power uctuations using fuzzy logic pitch angle con-troller. Simulation results indicate the effectiveness of thecontroller in smoothing output power uctuations with a verysmall drop of output power.

Kasiri et al. [116] have obtained fuzzy rules from neuralnetwork using genetic algorithm. The Fuzzy Rule Extraction fromNeural network using Genetic Algorithm (FRENGA) rejects winddisturbance in Wind Energy Conversion Systems (WECS) inputwith pitch angle control generation. A sensorless MPPT fuzzycontroller is developed for a doubly fed induction generator10 W (DFIG) wind turbine system by Bezza et al. [215]. The modelwas validated using MATLAB/Simulink. An adaptive lter withfuzzy logic based MPPT controller is used for handling theuctuation of wind speed and wind rotor inertia for small-scaleWECS by Narayana et al. [216]. Hamane et al. [217] have comparedtwo types of controllers PI and fuzzy PI for wind energyconversion systems (WECS) in terms of tracking and robustnessof the system. Reference tracking and robustness was checkedusing simulation analysis. Kairous and Wamkeue [218] have usedthe sliding mode control technique with fuzzy logic to study theperformance of a DFIG of WECS. The effectiveness of the proposedDFIG WECS control is compared with ywheel energy storagesystem (FESS) using computer simulation. The results show that infuzzy controlled SMC there is enhancement of power quality andproduce more clean power to the grid. Qi and Meng [219] haveapplied the theory of fuzzy control and PID control to the controlof generator speed and blade pitch angle of a wind turbine.MATLAB simulation was carried out in the mathematical model.The above review presented the various fuzzy based NN, GA, PSOtechniques and how these models helped in the optimal function-ing of the system.

To derive maximum power from the wind energy system,studies have been carried out to compare the energy output fromdifferent types of wind energy conversion systems by adoptingdifferent controllers/generators/pitch angle/wind speed etc. Fuzzybased systems are compared and analysed. Pichan et al. [220] haveproposed two fuzzy logic based direct power control (DPC)strategies for wind energy conversion system. The fuzzy DPC arefound to be robust against machine parameters mismatches andgrid voltage disturbances. The effectiveness of the proposedmethod is conrmed using MATLAB/Simulink under transientand steady state condition. Eltamaly and Farh [221] have proposeda variable speed control scheme that adopts fuzzy logic forobtaining maximum power from a grid connected wind energyconversion system. Two computer simulation packages (PSIM andSimulink) are used to carry out the simulation effectively. Aissaouiet al. [222] have developed a fuzzy-PI speed controller to extractoptimal power fromwind turbines. The simulation results conrmthe effectiveness of adaptive fuzzy PI speed controller. Deraz andKader [223] have suggested a current controlled voltage sourceinverter (CC-VSI) with an electronic load controller (ELC). This isbased on fuzzy logic control principles. Three fuzzy logic PIcontrollers and one hysteresis current controller are used toextract the maximum energy from the wind turbine. Abdeddaimand Betka [224] have proposed two objectives a MPPT algorithmbased on fuzzy logic to optimally extract maximum energy fromDFIG wind turbine; and a sliding mode control to achieve smoothregulation of both stator active and reactive power quantities. In


all instances it was found that fuzzy logic integrated systems gavebetter performance.

Fuzzy logic based power management strategy was investi-gated with various types of generators namely DFIG, SCIG. Sarrias-Mena et al. [225] proposed power management strategies for windturbine system working on a multi-MW doubly fed inductiongenerator (DFIG). Fuzzy logic based MPPT method is used forextracting maximum power from a wind turbine system driven bya DFIG [226] while Varzaneh et al. [227] proposed a fuzzy PIDcontroller with a DFIG for a variable speed wind farm. The fuzzyPID controller helped in delivering optimal rotational speed of theturbine and smoothening of output power. A rotor inertia is usedfor energy storage. In order to improve the power output ofSquirrel-Cage Induction Generator Wind Turbine (SCIG WT)Duong et al. [228] have proposed a hybrid controller for pitchangle based on PI and fuzzy technique. Considering the variablespeed from wind turbine the research presented the merits anddemerits of a certain type of generator.

For xed speed wind energy conversion system Krichen et al.[229] have presented the design of a fuzzy logic supervisor for thecontrol of active and reactive power. It was found that a gooddamping performance has been achieved with the fuzzy super-visor during wind speed variations. Jerbi et al. [230] have workedon fuzzy logic for a variable wind speed conversion system. Afuzzy logic supervisor is established to control the FlywheelEnergy Storage System (FESS) operation and the DC bus voltagein order to smooth the active power uctuations due to therandom wind speed variation. The authors state that the resultsshow an enhancement of the power quality. The research revealsthat fuzzy logic controllers are very adaptive to both variablespeed and xed speed wind energy conversion systems.

Fuzzy logic methodology is very versatile in handling uncer-tainties and unknown parameters since it captures human reason-ing and tries to mime human beings. Kamal and Aitouche [231]have proposed a Fuzzy Dedicated Observers (FDOS) method. Theyhave adopted a Nonlinear Unknown Input Fuzzy Observer (UIFO)with a Fuzzy Scheduler Fault Tolerant Control (FSFTC) algorithmfor fuzzy TakagiSugeno (TS) systems. This is subject to sensorfaults, parametric uncertainties, and time varying unknowninputs. The effectiveness of the system is tested using a windenergy systemwith doubly fed induction generators (DFIG). Kamalet al. [232] have proposed a fuzzy scheduler fault tolerant controlto deal with the multivariable non-linear systems that is subject ofsensor faults, actuator faults and parameter uncertainties. TakagiSugeno fuzzy model has been employed to represent the nonlinearwind energy systems. Since there are several unknowns present inthese systems TakagiSugeno fuzzy models are found to be veryappropriate as it handles both fuzzy and crisp values.

Wang and Xiong [51] have used a hybrid forecasting modelcomprising of outlier detection and bivariate fuzzy time series topredict the daily wind speed in one of the China's largest windfarm. Fuzzy rough sets consisting of fuzzy partition, fuzzy approx-imation and estimation of regression values were also used foraccurate wind speed prediction [49]. The methodology using fuzzyset theory has been established as an accurate forecasting tool.

Sfetsos [82] have performed a comparative study of variousforecasting techniques such as time series, ARMA (autoregressivemoving average Box Jenkins) feed forward and Elman recurrentneural networks including ANFIS and Neural Logic Networks onmean hourly wind speed data. It was found that the AI based models(namely fuzzy logic, neural networks) gave better results and amongthe AI models, Neural Logic Network gave the least error. Ustuntasand Sahin [99] have found cluster center fuzzy logic model givesbetter accuracy than classical least square polynomial model forestimation of the power curve of a wind turbine. An adaptive neurofuzzy control [67] is used to optimize the use of wind energy in smart

grids. The proposed system used wireless coded power control withquarternary phase shift-keying modulation and low density paritycheck. The system is found to improve system robustness andreliability. The review further conrms how fuzzy integrated modelscan be used as a good predictive and optimization tool in windenergy systems as seen in solar energy systems.

Advanced complex techniques such PSO, GA, recursive LS andTSK have all adopted fuzzy logic integrated algorithms to enhancethe functioning and performance of systems. Wang and Singh[134] have used multi-objective particle swarm optimization(MOPSO) algorithm to balance system risk and operational cost.Fuzzy membership functions are used to capture the dispatcher'sattitude in economic power dispatch toward the wind powerpenetration. A TakagiSugenoKang fuzzy model has been usedfor extracting maximum energy from variable wind speed tur-bines. To arrive at the fuzzy model Calderaro et al. [97] have usedfuzzy clustering methods for partitioning the inputoutput space,genetic algorithm (GA) and recursive least square (LS) optimiza-tion methods. The adaptive characteristic of the fuzzy controller isfound to continuously optimize its internal parameters in order tocompensate for all the non-linearities and the time variances ofthe system under control. Monfared et al. [76] have predicted windspeed using fuzzy logic and articial neural network. The authorsstate that the model provides signicantly less rule base andincreased estimated wind speed accuracy when compared totraditional models. The experiment results indicate that theproposed model performs in less computational time with betterwind speed prediction. Galdi et al. [233] have adopted an adaptiveTakagiSugenoKang (TSK) fuzzy model for extracting maximumenergy from variable speed wind power generation systems. Thedesign methodology uses fuzzy clustering to partition the data andis then integrated with GA and recursive LS optimization. Fuzzyreasoning is used to dene output power control corresponding towind speed condition and changing capacity of power system bySenjyu et al. [234]. The simulation results highlight the effective-ness of the proposed metho

applications of fuzzy logic in renewable energy systems – a review

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