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Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Author

Listed:
  • Habib Kraiem

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Processes, Energy, Environment and Electrical Systems, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia)

  • Ezzeddine Touti

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Laboratory of Industrial Systems and Renewable Energies, National Higher Engineering School of Tunis, Tunis 1008, Tunisia)

  • Abdulaziz Alanazi

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia)

  • Ahmed M. Agwa

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt)

  • Tarek I. Alanazi

    (Department of Physics, College of Science, Northern Border University, Arar 73222, Saudi Arabia)

  • Mohamed Jamli

    (Laboratory of Industrial Systems and Renewable Energies, National Higher Engineering School of Tunis, Tunis 1008, Tunisia)

  • Lassaad Sbita

    (Processes, Energy, Environment and Electrical Systems, National Engineering School of Gabes, University of Gabes, Gabes 6029, Tunisia)

Abstract

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

Suggested Citation

  • Habib Kraiem & Ezzeddine Touti & Abdulaziz Alanazi & Ahmed M. Agwa & Tarek I. Alanazi & Mohamed Jamli & Lassaad Sbita, 2023. "Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10510-:d:1186378
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    References listed on IDEAS

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