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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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    1. Vincenzo Franzitta & Aldo Orioli & Alessandra Di Gangi, 2017. "Assessment of the Usability and Accuracy of Two-Diode Models for Photovoltaic Modules," Energies, MDPI, vol. 10(4), pages 1-32, April.
    2. Ishaque, Kashif & Salam, Zainal & Lauss, George, 2014. "The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions," Applied Energy, Elsevier, vol. 119(C), pages 228-236.
    3. Ishaque, Kashif & Salam, Zainal & Shamsudin, Amir & Amjad, Muhammad, 2012. "A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 99(C), pages 414-422.
    4. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    5. 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.
    6. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    7. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
    8. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    9. Mamarelis, Emilio & Petrone, Giovanni & Spagnuolo, Giovanni, 2014. "A two-steps algorithm improving the P&O steady state MPPT efficiency," Applied Energy, Elsevier, vol. 113(C), pages 414-421.
    10. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    11. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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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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