This study presents the parameter extraction of single, double, and triple‐diode photovoltaic (PV) models using the weighted leader search algorithm (WLS). The primary objective is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different PV cells, and 18 for six PV modules, whose experimental data are publicly available, are developed successfully. The second objective of this research is the selection of the most suitable algorithm for this problem. It is a significant challenge since the evaluation process requires using advanced statistical tools and techniques to determine the reliable selection. Therefore, seven brand‐new algorithms, including WLS, the spider wasp optimizer, the shrimp and goby association search, the reversible elementary cellular automata, the fennec fox optimization, the Kepler optimization, and the rime optimization algorithms, are tested. The WLS has yielded the smallest minimum, average, RMSE, and standard deviation among those. Its superiority is also verified by Friedman and Wilcoxon signed‐rank test based on 144 pairwise comparisons. In conclusion, it is demonstrated that the WLS is a superior algorithm in PV parameter extraction for developing accurate models.

中文翻译：

---

使用加权前导搜索算法提取单、双和三二极管光伏模型的参数


本研究提出了使用加权前导搜索算法 (WLS) 来提取单二极管、双二极管和三二极管光伏 (PV) 模型的参数。主要目标是开发准确反映光伏设备特性的模型，以便在各种约束下实现技术和经济效益的最大化。为此，成功开发了24个模型，其中6个模型适用于两种不同的光伏电池，18个模型适用于6个光伏组件，其实验数据已公开。这项研究的第二个目标是选择最适合该问题的算法。这是一个重大挑战，因为评估过程需要使用先进的统计工具和技术来确定可靠的选择。因此，对WLS、蜘蛛黄蜂优化器、虾虎鱼关联搜索、可逆初等元胞自动机、耳廓狐优化、开普勒优化和雾凇优化算法等七种全新算法进行了测试。 WLS 产生了其中最小的最小值、平均值、RMSE 和标准差。基于 144 次配对比较的 Friedman 和 Wilcoxon 符号秩检验也验证了其优越性。总之，事实证明，WLS 是一种用于开发准确模型的 PV 参数提取的优越算法。