A GA-based optimization of a fuzzy-based MPPT controller for a photovoltaic pumping system, Case study for Laghouat, Algeria

Hadjaissa, A.; Cheikh, Salah Med Ait; Ameur, Khaled; Essounbouli, Najib

doi:10.1016/j.ifacol.2016.07.791

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…22,37,38 Rules connect the input values with the output values using "IF-Then" statement and "AND," "OR" or "NOR" Booleans. 55,56 Hence, rules are the link between the input and corresponding output values. Less number of data points result in smaller number of rules hence, for some imported input values it may predict inaccurate results.…”

Section: Fuzzy Logic (Fl)mentioning

confidence: 99%

“…It is found that, fuzzy-GA MPPT technique results in high power generation and enhancement in maximum efficiency of system compared to fuzzy MPPT technique. 56 Adaptive models are integrated with GA by Xia et al for forecasting and optimization of the energy performance of solar photovoltaic thermal collectorbased ground source heat pump. Proposed optimized model results in saving of 7.8%, 7.1% and 7.5% in the energy consumption during cooling, heating and transition period, respectively as well as enhancement of 4.4%, 6.2% and 5.1% in electricity generation during cooling, heating and transition period, respectively.…”

Section: Prediction Of Performance Characteristics Of Solar Photovomentioning

confidence: 99%

“…To assure the accuracy of formulated fuzzy model, data set is split into two‐parts, training and validation data sets 22,37,38 . Rules connect the input values with the output values using “IF‐Then” statement and “AND,” “OR” or “NOR” Booleans 55,56 . Hence, rules are the link between the input and corresponding output values.…”

Section: Artificial Intelligence (Ai) Techniquesmentioning

confidence: 99%

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A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Fuzzy Logic (Fl)mentioning

confidence: 99%

Section: Prediction Of Performance Characteristics Of Solar Photovomentioning

confidence: 99%

Section: Artificial Intelligence (Ai) Techniquesmentioning

confidence: 99%

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A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Due to the lack of meteorological databases, an estimated scenario of the solar irradiance is made by using Lieu & Jordan model. The location of estimated solar energy is chosen to be at Laghouat city, Algeria , Figure (a).…”

Section: Simulation and Resultsmentioning

confidence: 99%

Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

Ameur

Hadjaissa

Cheikh

et al. 2017

Int. J. Energy Res.

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Summary In this paper, a fuzzy energy management algorithm for a hybrid renewable power system based on lifetime extending is presented. When the system contains two storage elements or more, the selection of the suitable element to be charged or discharged becomes of paramount importance. When the storage elements are of different types, the decision will be difficult. Conventional algorithms that make series of tests to select the storage element choose always the first available element. This way of testing affects badly the most used element and may affect the other storage elements too as they rarely operate under hard load scenarios. In this study, and in order to solve this problem, two fuzzy controllers have been used to manage the energy flow for a hybrid renewable power system. It is composed of: a photovoltaic generator as a main source, a fuel cell and batteries as a storage elements. The controllers operate as master and slave. The master controller gives orders to all the system power converters and to the slave controller as well. The latter is activated only when the storage elements are at the same state of charge. It is charged, instead of the master's, to select the suitable element to be charged or discharged. Its orders are given based on lifetime functions for each element. To examine the proposed algorithm, simulations have been performed under Matlab/Simulink (The MathWorks, Inc., Massachusetts, USA). Comparison and statistics have been carried out to give the percentage of the worked hours for each element in each operating mode. The obtained results show the high performance of the proposed algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Such non-linear and non-minimum phase characteristics further confuse the MPPT of the boost converter [5]. To overcome these problems, different conventional and intelligent MPPT algorithms have been proposed such as Incremental Conductance (IC) [6][7][8], Open Circuit Voltage (OCV) [9], Short Circuit Current (SCC) [10], Perturb and Observe (P&O) [11], fuzzy logic [12][13][14][15], feedback linearization [16], neural network [17][18][19][20][21][22], neuro-fuzzy [23][24][25] and sliding mode [26,27]. Nevertheless, there still remains the concern of fast and accurately determining the locus of the MPP during high weather variations and external load changes occurring.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

Hassan

Kamal

et al. 2017

Energies

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An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

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A GA-based optimization of a fuzzy-based MPPT controller for a photovoltaic pumping system, Case study for Laghouat, Algeria

Cited by 14 publications

References 12 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Fuzzy energy management of hybrid renewable power system with the aim to extend component lifetime

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

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