Model Parameter Extraction of Solar PV Cell Using Gold Rush Optimizer

In this article, a recently developed metaheuristic optimization algorithm, Gold Rush Optimizer (GRO) is applied in extracting five parameters of solar photovoltaic (PV) cells. The mathematical model of a PV cell is considered to be a highly complex and non-linear and extraction of model parameters is found to be the multi-modal and multivariate problem. It is difficult to solve this problem using conventional methods. Metaheuristic algorithms have the advantage of solving this problem of parameter extraction. Therefore, in this paper, GRO with Newton Raphson (NR) method has been used to extract the parameters and is compared with four state-of-art algorithms such as grey wolf optimization (GWO), harris hawk optimization (HHO), bald eagle search (BES), and whale optimization algorithm (WOA). The parameter extraction problem is characterized by root mean squared error (RMSE)-based objective function. Two case studies have been considered to evaluate GRO’s effectiveness. Results reveal that with RMSE value of 7.72E-4 and 1.59E-04 for case study 1 and 2 respectively GRO identifies solar PV parameters accurately. Also, the algorithm’s accuracy has been checked through the closeness between estimated and experimental I-V characteristics for both case studies. Moreover, convergence curves have been plotted to evaluate the convergence speed, and the effectiveness is evaluated using statistical study including min, standard deviation, and mean of RMSE value. It is observed that GRO extracted model parameters of solar PV cell accurately and statistical study reveals that GRO outperforms among other algorithms.