Mayfly Optimization Algorithm–Based PV Cell Triple-Diode Model Parameter Identification

Abstract

The maximum power output and control optimization analysis of photovoltaic (PV) systems are based on accurate and reliable PV cell parameter identification. However, its difficult problems such as high nonlinearity and multimodality have become obstacles to the traditional optimization methods to obtain accurate and efficient results. This study uses a new intelligent optimization algorithm called the mayfly algorithm (MA) to efficiently identify the triple-diode model (TDM) of PV cells and uses the minimum root mean square error (RMSE) as the evaluation index to verify the effectiveness of the algorithm. Moreover, by continuously adjusting the parameters, population number, and iteration times of the MA to better balance the relationship between global development and local optimization, we can obtain more efficient and better optimization results. The research case shows that the MA is superior to other meta-heuristic algorithms in the accuracy and stability of PV cell parameter identification. For example, the minimum standard deviation (SD) of the RMSE obtained by the MA is 1,305 times smaller than another algorithm.