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Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization

Author

Listed:
  • Yacine Bouali

    (Department of Electrical Engineering, University of Science and Technology Houari Boumediene, P.O. Box 32, El-Alia, Algiers 16111, Algeria)

  • Basem Alamri

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation.

Suggested Citation

  • Yacine Bouali & Basem Alamri, 2024. "Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization," Mathematics, MDPI, vol. 13(1), pages 1-39, December.
  • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:19-:d:1552938
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    References listed on IDEAS

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    1. Rabeh Abbassi & Salem Saidi & Shabana Urooj & Bilal Naji Alhasnawi & Mohamad A. Alawad & Manoharan Premkumar, 2023. "An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models," Mathematics, MDPI, vol. 11(22), pages 1-21, November.
    2. Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    3. Jerry L. Holechek & Hatim M. E. Geli & Mohammed N. Sawalhah & Raul Valdez, 2022. "A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
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