Parameter Extraction of Single, Double, and Triple-Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

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. The photovoltaic (PV) parameter extraction problem' aim is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. The PV parameter extraction is studied and seven brand-new algorithms are used. The evaluation metrics, statistical tests, and sensitivity analysis show that the weighted leader search algorithm is a reliable alternative. image