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A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

Author

Listed:
  • Samia Charfeddine

    (Research Unit of Photovoltaic, Wind and Geothermal Systems, National Engineering School of Gabès, University of Gabès, Gabès 6029, Tunisia)

  • Hadeel Alharbi

    (Department of Computer Science, College of Computer Science and Engineering, University of Hail, Hail 1234, Saudi Arabia)

  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia)

  • Mourad Kchaou

    (Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia)

  • Rabeh Abbassi

    (Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia)

  • Víctor Leiva

    (School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

Abstract

Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation.

Suggested Citation

  • Samia Charfeddine & Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Víctor Leiva, 2022. "A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems," Mathematics, MDPI, vol. 10(12), pages 1-26, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2128-:d:842429
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    References listed on IDEAS

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    1. Naoui Mohamed & Flah Aymen & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "Efficient Power Management Strategy of Electric Vehicles Based Hybrid Renewable Energy," Sustainability, MDPI, vol. 13(13), pages 1-20, June.
    2. Dizqah, Arash M. & Maheri, Alireza & Busawon, Krishna, 2014. "An accurate method for the PV model identification based on a genetic algorithm and the interior-point method," Renewable Energy, Elsevier, vol. 72(C), pages 212-222.
    3. Ebrahimi, S. Mohammadreza & Salahshour, Esmaeil & Malekzadeh, Milad & Francisco Gordillo,, 2019. "Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm," Energy, Elsevier, vol. 179(C), pages 358-372.
    4. Hanen Chaouch & Samia Charfeddine & Sondess Ben Aoun & Houssem Jerbi & Víctor Leiva, 2022. "Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
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    Cited by:

    1. Obaid Alshammari & Mourad Kchaou & Houssem Jerbi & Sondess Ben Aoun & Víctor Leiva, 2022. "A Fuzzy Design for a Sliding Mode Observer-Based Control Scheme of Takagi-Sugeno Markov Jump Systems under Imperfect Premise Matching with Bio-Economic and Industrial Applications," Mathematics, MDPI, vol. 10(18), pages 1-28, September.
    2. Tamás Orosz & Anton Rassõlkin & Pedro Arsénio & Peter Poór & Daniil Valme & Ádám Sleisz, 2024. "Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels," Energies, MDPI, vol. 17(6), pages 1-22, March.

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