Abstract
Photovoltaic (PV) technology can convert solar energy to electric power, which is an essential tool for future years. Subsequently, several static solar PV models have been designed to simulate the current in a PV cell. However, the modeling process of PV systems requires extracting the unknown parameters of these cells, which can be modeled as an optimization problem. However, this is a very challenging task as it is multimodal and nonlinear. In this case, a Laplacian Nelder-Mead hunger games search (HGS) method, denoted as LNMHGS, is proposed for parameter extraction of static solar PV models. It realizes the equilibrium between exploitation and exploration by introducing the Laplacian strategy and Nelder-Mead simplex mechanism to hunger name search. LNMHGS compares against many recent methods and stands out by efficiently estimating static PV models' parameters. The simulation outcomes show that the root mean square error (RMSE) values and standard devi-ation offered by LNMHGS are smaller than other algorithms. Meanwhile, LNMHGS obtains the best performance both in different light conditions and at different temperature conditions. Therefore, LNMHGS is a promising, reliable, and feasible alternative for optimizing the parameters for PV systems.