Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm

Abstract

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. The original differential evolution (DE) is good at exploring the search space and locating the region of global optimum, but it is slow at exploitation of the solutions. Quite the opposite, the original whale optimization algorithm (WOA) is good at exploiting the population information, but it easily suffers from premature convergence. In such a context, in this paper, an effective hybrid method named DE/WOA by combining the exploration of DE with the exploitation of WOA is proposed for extracting the accurate parameters of PV models. A set of 13 numerical benchmark functions with different characteristics is firstly employed to verify the performance of DE/WOA. Then, DE/WOA is applied to parameter extraction of three PV models, i.e., single diode, double diode, and PV module models. Finally, DE/WOA is implemented to a practical PV power station in the Guizhou Power Grid of China to further validate its effectiveness under different irradiances, temperatures, and dynamic weather conditions. All the experimental results comprehensively demonstrate that DE/WOA performs significantly better than the original DE, WOA, and five advanced variants of them and is highly competitive with some recently-proposed parameter extraction methods.