Hybrid Artificial Ecosystem-Based Optimization Algorithm Solves Photovoltaic Models Parameter Identification Problem

Photovoltaic (PV) models have both nonlinear and multimodal characteristics, and traditional algorithms are prone to fall into local optimum and insufficient recognition accuracy in identifying their parameters. This paper proposes a hybrid artificial ecosystem-based optimization (HAEO) algorithm for fast, accurate, and reliable identification of PV model parameters under different environmental conditions. In HAEO, aNelder-Mead polyhedral search strategy is used to update the poorer individuals. In addition, a chaotic local search strategy is introduced to enhance the ability of populations to jump out of the local optimum by using the ergodic and non-repetitive nature of chaos. To verify the effectiveness of the proposed algorithm, it is used for the parameter identification of thin-film ST40 PV modules under different irradiation intensities and temperatures conditions. Simulation results show that the HAEO algorithm is more competitive than the existing algorithms in terms of accuracy and reliability. Therefore, HAEO can be considered an effective PV model parameter identification method.