Optimal estimation of parameters of the three-diode commercial solar photovoltaic model using an improved Berndt-Hall-Hall-Hausman method hybridized with an augmented mountain gazelle optimizer

Abstract

It is absolutely necessary to extract the photovoltaic (PV) model parameters to anticipate the energy production of PV systems accurately. In the literature, many studies have analyzed and discussed various strategies for handling the parameter computation of the PV model. However, very few studies have been conducted to formulate the fitness function, and no studies have been presented on the methodologies to solve the nonlinear, multivariable, and complicated PV models based on empirical data. As a result, the key objective is to investigate the traditional methods for solving the equations of PV models. An improved variant of the Mountain Gazelle Optimizer (MGO) called Augmented Mountain Gazelle Optimizer (AMGOIB3H) is proposed to guarantee MGO convergence based on an improved Berndt-Hall-Hall-Hausman method. This AMGOIB3H highlights key advancements in the literature regarding improving the exploration and exploitation phases of MGO and the design of objective functions. Finally, a hybrid method has been established for effectively identifying unknown parameters of the three-diode PV model. This method uses actual measured laboratory data gathered under various environmental conditions. The simulation results show that the AMGOIB3H reduces errors to zero under various statistical standards and environmental variables. In addition, the AMGOIB3H outperforms the state-of-the-art algorithm in the research literature regarding reliability, accuracy, and convergence rate with a reasonable processing time.