High-Performance Technique for Estimating the Unknown Parameters of Photovoltaic Cells and Modules Based on Improved Spider Wasp Optimizer

Abstract

To better estimate the unknown parameters of the double-diode model, a new optimization technique based on the newly proposed spider wasp optimizer (SWO) is introduced in this study. The performance of SWO was further enhanced by integrating it with a local search strategy to propose a new improved variant called ISWO. This improved variant has a high ability to extensively exploit the solutions surrounding the best-so-far solution in an effort to speed up convergence and produce better results in fewer function evaluations. Using the RTC France solar cell and three PV modules (STM6-40/36, STP6-120/36, and Kyocera KC200GT), ISWO and SWO are evaluated and compared to four well-known metaheuristic optimization methods. The objective values acquired by those algorithms in thirty separate runs are examined using the Wilcoxon rank sum test and a number of performance measures. The experimental findings demonstrate ISWO's exceptional performance for every PV module under consideration.