Design and implementation of reconfigurable MPPT fuzzy controller for photovoltaic systems

Loukil, Kais; Abbes, Hanen; Abid, Hafedh; Abid, Mohamed; Toumi, Ahmed

doi:10.1016/j.asej.2019.10.002

Cited by 63 publications

(31 citation statements)

References 17 publications

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“…134 Loukil et al have also proposed FL controller for tracking maximum point at which photovoltaic system generates maximum power. 135 To enhance the accuracy of MPPT in photovoltaic systems, Guenounou et al have developed adaptive FL controller by incorporating effect of adaptive gain α in the conventional FL controller. Adaptive FL controller is formulated using triangular + trapezoidal membership functions such that, each input and change of duty cycle consist of five membership functions and adaptive gain α comprised of six membership functions along with 25 rules to relate the input-output variables.…”

Section: Prediction Of Performance Characteristics Of Solar Photovomentioning

confidence: 99%

“…Zou et al have proposed FL‐based controller for efficient MPPT of solar PV system under partial shading conditions 134 . Loukil et al have also proposed FL controller for tracking maximum point at which photovoltaic system generates maximum power 135 . To enhance the accuracy of MPPT in photovoltaic systems, Guenounou et al have developed adaptive FL controller by incorporating effect of adaptive gain α in the conventional FL controller.…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

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: Prediction Of Performance Characteristics Of Solar Photovomentioning

confidence: 99%

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

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

2020

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“…Proposed adoption of a fuzzy logic system in the actual implementation of the MPPT controller. Was tested under various environmental limitations [19].…”

Section: Methods Adopted Inferencesmentioning

confidence: 99%

Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment

Anthony¹,

Prasad

Kannadasan

et al. 2021

Electronics

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This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.

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“…In recent years, many advanced control techniques have been associated with the MPPT control such as fuzzy logic control (FLC) in order to increase the efficiency of solar panels. Several FLC methods are compared and reviewed in the literature (Dounis et al, 2013;Boukenoui et al, 2016;Kwan and Wu, 2016;Ouchen et al, 2016;Mohamed et al, 2017;Nabipour et al, 2017;Yilmaz et al, 2018;Youssef et al, 2018;Li X. et al, 2019c;Loukil et al, 2020;Verma et al, 2020;Jin et al, 2021;Rajesh et al, 2021;Tang et al, 2021). In this paper, four FLC methods are selected for comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study on Maximum Power Point Tracking Techniques Based on Fuzzy Logic Control for Solar Photovoltaic Systems

Zhu

Dong

et al. 2021

Front. Energy Res.

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Maximum power point tracking (MPPT) methods based on fuzzy logic control (FLC) is a popular application in recent years. However, different kinds of fuzzy control methods lack comparative study, which led to confusion in practice. Hence, a comprehensive study on these methods is essential. Unfortunately, very few attempts have been made in this regard. In this paper, four FLC methods are selected for comparative analysis. Furthermore, the design details and experimental result will also be given to help choose and measure these methods, which presents a clear image of the technology of FLC based MPPT to readers.

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Design and implementation of reconfigurable MPPT fuzzy controller for photovoltaic systems

Cited by 63 publications

References 17 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

Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment

A Comprehensive Study on Maximum Power Point Tracking Techniques Based on Fuzzy Logic Control for Solar Photovoltaic Systems

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