Gold Rush Optimizer for parameter extraction in double diode models of photovoltaic modules

The precision of electrical model characterization for photovoltaic (PV) installations is an essential task for the advancement of PV power technology. There are several models in the literature, each with many unknown parameters, and the precise determination of these parameters is a significant challenge. In this context, this paper explores the performance of the Gold Rush Optimizer (GRO) algorithm for dual diode model parameter extraction. The algorithm performance is evaluated to extract all the parameters of the dual-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 GRO was compared with that of other algorithms, such as JAYA and particle swarm optimization (PSO). The results show that the GRO algorithm achieves the lowest mean square error of 9.8272E-04 and the lowest standard deviation of 8.0939E-05. These results demonstrate that this optimization approach outperforms competing methods in terms of accuracy, efficiency, and stability. Furthermore, the GRO algorithm demonstrated superior accuracy compared to the JAYA and PSO algorithms across the entire voltage range. Indeed, this highlights the ability of GRO to maintain better accuracy between experimental and simulated data throughout the voltage range.