Evaluation of constraint in photovoltaic models by exploiting an enhanced ant lion optimizer

Abstract

Parameter estimation of photovoltaic models is a critical step in the control and management of photovoltaic equipment. In this study, to estimate photovoltaic model parameters efficiently and accurately, an enhanced Ant Lion Optimizer is designed, which is on account of the opposition-based learning mechanism and the Nelder-Mead simplex technique. This optimizer has a mediocre performance and suffers from high uncertainty in finding global optima, immature convergence, and imbalanced exploration and exploitation inclinations. Hence, the opposition-based learning mechanism is used to ensure in-depth exploration and achieve a better balance between diversification and intensification. The Nelder-Mead simplex is adapted to enable a smooth transition from extensive exploration to intensified exploitation. The proposed methodology is utilized to determine the parameters of photovoltaic solar cells using three diode models (i.e., single diode, double diode, and photovoltaic module). Besides, the performance of the proposed approach is validated based on three practical manufacturers’ datasets. The extensive experimental results show that the enhanced optimizer can estimate the parameters efficiently. It significantly outperforms a variety of well-known algorithms as a potential tool for parameter estimation of photovoltaic models and shows promising capability. A public online service supports this research for any question and application of the proposed tool at http://aliasgharheidari.com.