Parameter Identification of Solar Cell Mathematical Models Using Metaheuristic Algorithms

Abstract

Lately, there has been a notable interest among researchers in constructing a precise mathematical representation using experimentally gathered data from solar cells and photovoltaic (PV) modules. This representation serves as a means to simulate and assess the performance of PV systems. In this study, the Walrus Optimization Algorithm (WaOA) and Cheetah optimizer (CO) were employed to deduce the unknown parameters inherent in various modes of solar cells and PV modules, specifically the single-diode model (SDM) and double-diode model (DDM). Furthermore, the evaluation criterion for this work involved measuring the route mean square error (RMSE) between the simulated outcomes generated using identified parameters for each mathematical model and the actual voltage derived from measurements of solar cells and PV modules. Notably, a comprehensive statistical analysis was carried out to validate the efficacy and stability of the WaOA and CO algorithms. These algorithms were compared against other optimization techniques for their effectiveness in solving the optimization challenge of accurately estimating the design parameters of PV systems. The outcomes of simulations and the extensive statistical assessment substantiate the superior performance and reliability of the Walrus Optimization Algorithm in effectively extracting parameter values from diverse PV modules under various operational scenarios.