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Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller

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  • Gul Filiz Tchoketch Kebir

    (Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
    Laboratoire des Dispositifs de Communication et de Conversion Photovoltaïque, Département d’Électronique, École Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200, Algerie)

  • Cherif Larbes

    (Laboratoire des Dispositifs de Communication et de Conversion Photovoltaïque, Département d’Électronique, École Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200, Algerie)

  • Adrian Ilinca

    (Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada)

  • Thameur Obeidi

    (Wind Energy Research Laboratory, Université du Québec à Rimouski, 300, Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
    Laboratoire des Dispositifs de Communication et de Conversion Photovoltaïque, Département d’Électronique, École Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200, Algerie)

  • Selma Tchoketch Kebir

    (Laboratoire des Dispositifs de Communication et de Conversion Photovoltaïque, Département d’Électronique, École Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200, Algerie)

Abstract

The Maximum Power Point Tracking (MPPT) strategy is commonly used to maximize the produced power from photovoltaic generators. In this paper, we proposed a control method with a fuzzy logic approach that offers significantly high performance to get a maximum power output tracking, which entails a maximum speed of power achievement, a good stability, and a high robustness. We use a fuzzy controller, which is based on a special choice of a combination of inputs and outputs. The choice of inputs and outputs, as well as fuzzy rules, was based on the principles of mathematical analysis of the derived functions (slope) for the purpose of finding the optimum. Also, we have proved that we can achieve the best results and answers from the system photovoltaic (PV) with the simplest fuzzy model possible by using only 3 sets of linguistic variables to decompose the membership functions of the inputs and outputs of the fuzzy controller. We compare this powerful controller with conventional perturb and observe (P&O) controllers. Then, we make use of a Matlab-Simulink ® model to simulate the behavior of the PV generator and power converter, voltage, and current, using both the P&O and our fuzzy logic-based controller. Relative performances are analyzed and compared under different scenarios for fixed or varied climatic conditions.

Suggested Citation

  • Gul Filiz Tchoketch Kebir & Cherif Larbes & Adrian Ilinca & Thameur Obeidi & Selma Tchoketch Kebir, 2018. "Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller," Energies, MDPI, vol. 11(12), pages 1-20, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3263-:d:185039
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    References listed on IDEAS

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    1. Patcharaprakiti, Nopporn & Premrudeepreechacharn, Suttichai & Sriuthaisiriwong, Yosanai, 2005. "Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system," Renewable Energy, Elsevier, vol. 30(11), pages 1771-1788.
    2. Farahat, M.A. & Metwally, H.M.B. & Abd-Elfatah Mohamed, Ahmed, 2012. "Optimal choice and design of different topologies of DC–DC converter used in PV systems, at different climatic conditions in Egypt," Renewable Energy, Elsevier, vol. 43(C), pages 393-402.
    3. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
    4. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    5. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
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    Cited by:

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    3. Tehzeeb-ul Hassan & Rabeh Abbassi & Houssem Jerbi & Kashif Mehmood & Muhammad Faizan Tahir & Khalid Mehmood Cheema & Rajvikram Madurai Elavarasan & Farman Ali & Irfan Ahmad Khan, 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller," Energies, MDPI, vol. 13(15), pages 1-20, August.
    4. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    5. Mostafa Bakkar & Ahmed Aboelhassan & Mostafa Abdelgeliel & Michael Galea, 2021. "PV Systems Control Using Fuzzy Logic Controller Employing Dynamic Safety Margin under Normal and Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-20, February.
    6. Sameh Mostafa & Abdelhalim Zekry & Ayman Youssef & Wagdi Refaat Anis, 2022. "Raspberry Pi Design and Hardware Implementation of Fuzzy-PI Controller for Three-Phase Grid-Connected Inverter," Energies, MDPI, vol. 15(3), pages 1-22, January.
    7. Javier Solano & Diego Jimenez & Adrian Ilinca, 2020. "A Modular Simulation Testbed for Energy Management in AC/DC Microgrids," Energies, MDPI, vol. 13(16), pages 1-23, August.
    8. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    9. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.

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