A new modified version of mountain gazelle optimization for parameter extraction of photovoltaic models

This study addresses the challenges in accurately estimating photovoltaic (PV) parameters for solar energy applications by enhancing parameter extraction processes to improve the efficiency of PV models. An information gap in PV solar cell and module parameters provided by vendors obstructs accurate simulation. Traditional numerical techniques face limitations in accurately solving complex nonlinear optimization problems. As a solution, metaheuristic algorithms, specifically the mountain gazelle optimizer, are proposed. To overcome limitations of the mountain gazelle optimizer, a pattern search algorithm is integrated for a more robust global and local search. Rigorous testing demonstrates superior performance in achieving lower best values and tighter standard deviations compared to existing algorithms, making it a promising and efficient optimizer for accurate parameter estimation in various solar cell and module models, including the R.T.C. France silicon solar cell and Photowatt-PWP201 PV module. The proposed optimizer excels in estimating parameters for both single diode, double diode, and PV module models, outperforming state-of-the-art algorithms and showcasing its potential for reliable and precise optimization in solar cell modeling applications.