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Black Widow Optimization Algorithm Used to Extract the Parameters of Photovoltaic Cells and Panels

Author

Listed:
  • Manoharan Madhiarasan

    (Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Daniel T. Cotfas

    (Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Petru A. Cotfas

    (Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania)

Abstract

The metaheuristic algorithms and their hybridization have been utilized successfully in the past to extract the parameters of photovoltaic (PV) cells and panels. The novelty of the paper consists of proposing the black widow optimization algorithm (BWOA) for the first time to identify the parameters of the two photovoltaic cells RTC France, amorphous silicon (aSi), and two photovoltaic panels PWP201, PVM 752 GaAs. The single-diode model (SDM) and double-diode model (DDM) for analyzing the PVs are considered. The performance of the BWOA is verified using four statistical tests: the root mean square error, which is the primary tool, the mean relative error, the mean bias error, and the coefficient of determination. The research results of this study are as follows: BWOA gave the same results, or very slightly better, for RTC and PWP201 for SDM in comparison with the best algorithms from the specialized literature; for all the other cases, BWOA has substantially better results, especially for PVM 752 GaAs, where the improvements in RMSE are: 16.5%, for PWP201: 6.25%, and for aSi: 5.3%, all for the DDM; the computing time is around 2 s, which is one of the lowest durations. A consistent study is made to optimize the accuracy and computational time in function of the number of iterations and population.

Suggested Citation

  • Manoharan Madhiarasan & Daniel T. Cotfas & Petru A. Cotfas, 2023. "Black Widow Optimization Algorithm Used to Extract the Parameters of Photovoltaic Cells and Panels," Mathematics, MDPI, vol. 11(4), pages 1-24, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:967-:d:1067497
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    References listed on IDEAS

    as
    1. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    2. Adrián F. Peña-Delgado & Hernán Peraza-Vázquez & Juan H. Almazán-Covarrubias & Nicolas Torres Cruz & Pedro Martín García-Vite & Ana Beatriz Morales-Cepeda & Juan M. Ramirez-Arredondo, 2020. "A Novel Bio-Inspired Algorithm Applied to Selective Harmonic Elimination in a Three-Phase Eleven-Level Inverter," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, December.
    3. Guojiang Xiong & Jing Zhang & Dongyuan Shi & Xufeng Yuan, 2019. "Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    4. Lin, Xiankun & Wu, Yuhang, 2020. "Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture," Energy, Elsevier, vol. 196(C).
    Full references (including those not matched with items on IDEAS)

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