Parameter Identification of Photovoltaic Models Using an Enhanced INFO Algorithm
Ying Chen, Peng Min, Huiling Chen, Tao Cheng, Zeye Long, Ali Asghar Heidari, Shuihua Wang, Yudong Zhang
Abstract
ABSTRACT Photovoltaic (PV) systems are electrical systems designed to convert solar energy into electrical energy. As a crucial component of PV systems, harsh weather conditions, photovoltaic panel temperature and solar irradiance influence the power output of photovoltaic cells. Therefore, accurately identifying the parameters of PV models is essential for simulating, controlling and evaluating PV systems. In this study, we propose an enhanced weighted‐mean‐of‐vectors optimisation (EINFO) for efficiently determining the unknown parameters in PV systems. EINFO introduces a Lambert W‐based explicit objective function for the PV model, enhancing the computational accuracy of the algorithm's population fitness. This addresses the challenge of improving the metaheuristic algorithms' identification accuracy for unknown parameter identification in PV models. We experimentally apply EINFO to three types of PV models (single‐diode, double‐diode and PV‐module models) to validate its accuracy and stability in parameter identification. The results demonstrate that EINFO achieves root mean square errors (RMSEs) of 7.7301E‐04, 6.8553E‐04 and 2.0608E‐03 for the single‐diode model, double‐diode model and PV‐module model, respectively, surpassing those obtained by using INFO algorithm as well as other methods in terms of convergence speed, accuracy and stability. Furthermore, comprehensive experimental findings on three commercial PV modules (ST40, SM55 and KC200GT) indicate that EINFO consistently maintains high accuracy across varying temperatures and irradiation levels. In conclusion, EINFO emerges as a highly competitive and practical approach for parameter identification in diverse types of PV models.