Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer

Abstract Precise estimation of critical parameters of the photovoltaic models is a highly demanding task in modeling and simulation of the photovoltaic systems. In this paper, a new enhanced optimization method is proposed to estimate the unknown parameters of photovoltaic modules. The proposed adaptive chaotic grey wolf optimization (ACGWO) algorithm is employed to estimate the parameters of solar cells models for single-diode, double-diode, three-diode. The suggested optimization method is obtained by combining the adaptive grey wolf optimization (AGWO) and chaotic grey wolf optimization (CGWO) algorithms. Minimization of Root Mean Squared Error (RMSE) as employed as the common objective function. Besides, the results are compared with some well-known algorithms. The results of RMSE are compared with commonly used objective functions such as the sum of squared error (SSE) and Maximal Absolute Error (MAE). The efficiency of the ACGWO method is analyzed, and the results of the simulation report the lowest values for RMSE and confirm accuracy, robustness, and high convergence speed in comparison with some well-known optimization methods. According to the results, the proposed ACGWO could outperform four other methods competitive based on RMSE values, and the best values are reported for the ACGWO method.