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Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation

Author

Listed:
  • Mouncef El Marghichi
  • Abdelilah Hilali
  • Abdelkhalek Chellakhi
  • Mohamed Makhad
  • Azeddine Loulijat
  • Najib El Ouanjli
  • Abdelhak Essounaini
  • Vikash Kumar Saini
  • Ameena Saad Al-Sumaiti

Abstract

Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces the Mother Tree Optimization with Climate Change (MTO-CL) algorithm to address this challenge by enhancing parameter estimation for a solar PV three-diode model. MTO-CL improves optimization performance by incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% of suboptimal solutions) and distortion (slight adjustments to 80% of remaining solutions). This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. Compared to seven alternative methods, MTO-CL shows superior performance in parameter estimation for various solar modules, including ST40 and SM55, across different irradiances and temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A to 0.0165A and Mean Squared Error (MSE) values between 6.2 × 10^−6 and 2.7 × 10^−4, while also significantly minimizing power errors, ranging from 22.86 mW to 239.40 mW. These results demonstrate MTO-CL’s effectiveness in improving the accuracy and reliability of PV system modeling, offering a robust tool for enhanced solar energy applications.

Suggested Citation

  • Mouncef El Marghichi & Abdelilah Hilali & Abdelkhalek Chellakhi & Mohamed Makhad & Azeddine Loulijat & Najib El Ouanjli & Abdelhak Essounaini & Vikash Kumar Saini & Ameena Saad Al-Sumaiti, 2025. "Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-31, March.
    • Handle: RePEc:plo:pone00:0318575
    DOI: 10.1371/journal.pone.0318575
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    References listed on IDEAS

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    1. Abu Danish Aiman Bin Abu Sofian & Hooi Ren Lim & Heli Siti Halimatul Munawaroh & Zengling Ma & Kit Wayne Chew & Pau Loke Show, 2024. "Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(4), pages 3953-3978, August.
    2. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
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