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<p>Effective parameter extraction in photovoltaic (PV) models is critical for accurate simulation, analysis, and management of PV systems, given the inherent nonlinearity and complexity of these models. This paper introduces a novel computational intelligence approach: the Wild Horse Optimizer with Alternating Strategy (WHO-AS). WHO-AS integrates three key innovations: enhanced population diversity mechanisms to prevent premature convergence, an adaptive parameter tuning strategy to balance exploration and exploitation, and a dynamic alternating strategy to navigate complex search spaces effectively. These innovations prevent premature convergence and improve optimization efficiency. WHO-AS demonstrates superior search performance, achieving 1st rank in both the CEC2022-10Dim (Friedman mean rank: 1.3333) and CEC2017-100Dim (Friedman mean rank: 1.7931) benchmark tests, outperforming eight state-of-the-art algorithms, including widely cited classics (PSO, WOA), high-performance optimizers (WHO, GWO, DBO), and emerging techniques (IVY, CFOA, AO). Furthermore, WHO-AS excels in parameter identification for five PV models (i.e., single diode, double diode, triple diode, four diode, and photovoltaic module models), attaining root-mean-square error values of 9.8602E-04, 9.8259E-04, 9.8286E-04, 9.8250E-04, and 2.4253E-03, respectively. These results demonstrate exceptional accuracy and reliability. Comparative analyses of I-V and P-V characteristic curves further validate WHO-AS’s robustness in capturing real-world PV behavior, solidifying its superiority over competing methods.</p>

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Parameter Identification of Solar Photovoltaic Cell Model Based on an Enhanced Swarm Intelligence Optimization with Adaptive Parameter Tuning and Alternating Strategies

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Kun Hu,
  • Chia-Hung Wang

摘要

Effective parameter extraction in photovoltaic (PV) models is critical for accurate simulation, analysis, and management of PV systems, given the inherent nonlinearity and complexity of these models. This paper introduces a novel computational intelligence approach: the Wild Horse Optimizer with Alternating Strategy (WHO-AS). WHO-AS integrates three key innovations: enhanced population diversity mechanisms to prevent premature convergence, an adaptive parameter tuning strategy to balance exploration and exploitation, and a dynamic alternating strategy to navigate complex search spaces effectively. These innovations prevent premature convergence and improve optimization efficiency. WHO-AS demonstrates superior search performance, achieving 1st rank in both the CEC2022-10Dim (Friedman mean rank: 1.3333) and CEC2017-100Dim (Friedman mean rank: 1.7931) benchmark tests, outperforming eight state-of-the-art algorithms, including widely cited classics (PSO, WOA), high-performance optimizers (WHO, GWO, DBO), and emerging techniques (IVY, CFOA, AO). Furthermore, WHO-AS excels in parameter identification for five PV models (i.e., single diode, double diode, triple diode, four diode, and photovoltaic module models), attaining root-mean-square error values of 9.8602E-04, 9.8259E-04, 9.8286E-04, 9.8250E-04, and 2.4253E-03, respectively. These results demonstrate exceptional accuracy and reliability. Comparative analyses of I-V and P-V characteristic curves further validate WHO-AS’s robustness in capturing real-world PV behavior, solidifying its superiority over competing methods.