A GRO metaheuristic algorithm for Parameters identification of a single diode in a photovoltaic model

Abstract

The identification of parameters for photovoltaic (PV) models as an optimization problem has garnered significant interest in the scientific literature. In this context, this article explores the performance of the Gold Rush Optimizer (GRO) algorithm for extracting the parameters of the single-diode model. To this end, we evaluated the GRO's ability to extract all parameters of the single-diode model of the R.T.C. solar cell, using the Root Mean Square Error (RMSE) and standard deviation (std) as performance measures. The performance of the GRO was compared with other algorithms such as JAYA and Particle Swarm Optimization (PSO). The results show that our algorithm achieves the lowest Root Mean Square Error of 9.8602E-04 and the lowest standard deviation of 9.4593E-14. These results demonstrate that this optimization approach outperforms competing methods in terms of accuracy, efficiency, and stability. Thus, the GRO method proves to be particularly effective and reliable for the accurate estimation of PV model parameters.