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Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Author

Listed:
  • Noureddine Bouarroudj

    (Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria)

  • Djamel Boukhetala

    (Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Rue des Frères OUDEK, El-Harrach, Alger 16200, Algeria)

  • Vicente Feliu-Batlle

    (School of Industrial Engineering, University of Castilla-La Mancha, Av. Camilo Jose Cela, S/N, C.P. 13001 Ciudad Real, Spain)

  • Fares Boudjema

    (Laboratoire de Commande des Processus, Ecole Nationale Polytechnique, 10 Rue des Frères OUDEK, El-Harrach, Alger 16200, Algeria)

  • Boualam Benlahbib

    (Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria)

  • Bachir Batoun

    (Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, Ghardaïa 47133, Algeria)

Abstract

In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.

Suggested Citation

  • Noureddine Bouarroudj & Djamel Boukhetala & Vicente Feliu-Batlle & Fares Boudjema & Boualam Benlahbib & Bachir Batoun, 2019. "Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System," Energies, MDPI, vol. 12(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2827-:d:250666
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    References listed on IDEAS

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    Cited by:

    1. Miaomiao Ma & Xiangjie Liu & Kwang Y. Lee, 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Noureddine Bouarroudj & Yehya Houam & Abdelhamid Djari & Vicente Feliu-Batlle & Abdelkader Lakhdari & Boualam Benlahbib, 2023. "A Linear Quadratic Integral Controller for PV-Module Voltage Regulation for the Purpose of Enhancing the Classical Incremental Conductance Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.
    3. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    4. Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    5. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.

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