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<p>Accurate parameter identification of photovoltaic (PV) models is critical for reliable simulation, performance prediction, and optimal operation of solar energy systems. Thus, this study introduces a novel, red-billed blue magpie algorithm (RBMA) to address the nonlinear nine-parameter identification problem of the PV triple-diode representation (TDR). Challengingly, RBMA is benchmarked against three competitive optimizers, black widow optimizer, grey wolf optimizer, and Runge-Kutta optimizer, across five well-known PV modules. The root means square deviation (RMSD) results demonstrate that RBMA consistently provides the lowest estimation error in all test cases. Numerically, RBMA obtained RMSD values of 0.742076&#xa0;mA, 2.050664&#xa0;mA, 28.09501&#xa0;mA, 1.684796&#xa0;mA, and 13.774&#xa0;mA for the R.T.C France, PWP201, KC200GT, STM6-40/36, and STP6-120/36, respectively. Notably, it achieves a 5.6% reduction in RMSD compared with the nearest competitor, in the KC200GT module, confirming a tangible improvement in estimation precision. In addition, statistical testing using the Friedman test and Wilcoxon signed-rank analysis validates the robustness and superiority of RBMA over the other algorithms. Furthermore, the RBMA-cropped parameters are validated by simulating the electrical behaviour of the STP6-120/36 module under variable irradiance and temperature, confirming the physical validity of the optimized model. These findings highlight the effectiveness of RBMA as a reliable computational tool for advanced photovoltaic modelling and parameter identification under practical operating conditions.</p>

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Robust optimization of photovoltaic triple diode model parameters via the red-billed blue magpie algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Hossam Ashraf,
  • Abdelmonem Draz,
  • Abdelfattah M. Elmoaty,
  • Attia A. El‑Fergany

摘要

Accurate parameter identification of photovoltaic (PV) models is critical for reliable simulation, performance prediction, and optimal operation of solar energy systems. Thus, this study introduces a novel, red-billed blue magpie algorithm (RBMA) to address the nonlinear nine-parameter identification problem of the PV triple-diode representation (TDR). Challengingly, RBMA is benchmarked against three competitive optimizers, black widow optimizer, grey wolf optimizer, and Runge-Kutta optimizer, across five well-known PV modules. The root means square deviation (RMSD) results demonstrate that RBMA consistently provides the lowest estimation error in all test cases. Numerically, RBMA obtained RMSD values of 0.742076 mA, 2.050664 mA, 28.09501 mA, 1.684796 mA, and 13.774 mA for the R.T.C France, PWP201, KC200GT, STM6-40/36, and STP6-120/36, respectively. Notably, it achieves a 5.6% reduction in RMSD compared with the nearest competitor, in the KC200GT module, confirming a tangible improvement in estimation precision. In addition, statistical testing using the Friedman test and Wilcoxon signed-rank analysis validates the robustness and superiority of RBMA over the other algorithms. Furthermore, the RBMA-cropped parameters are validated by simulating the electrical behaviour of the STP6-120/36 module under variable irradiance and temperature, confirming the physical validity of the optimized model. These findings highlight the effectiveness of RBMA as a reliable computational tool for advanced photovoltaic modelling and parameter identification under practical operating conditions.