Parameter Identification of Photovoltaic Models by an Enhanced RIME Algorithm

Accurate identification of photovoltaic (PV) model parameters is crucial for the study of factors affecting PV power generation efficiency. This study addresses this challenge by introducing the enhanced RIME algorithm (ERIME). ERIME achieves a fine balance between exploration and exploitation by combining a modified soft-rime search strategy and a shared information mutation mechanism, thus ensuring population diversity. To assess the efficacy of ERIME, the recognized CEC2022 test function suite is adopted to evaluate its search performance. Furthermore, to validate the versatility of ERIME in parameter identification of PV model, we tested the proposed method on several PV models, including single diode model (SDM), double diode model (DDM), triple diode model (TDM), and multiple PV module models (PVM). An in-depth statistical analysis of the results and a comparative study with the popular existing research methods highlight the superiority of ERIME. Specifically, ERIME improves the best value metrics of SDM, DDM, and TDM by 6.63E-06, 2.28E-05, and 1.36E-04, respectively, compared to RIME and reduces the standard deviation by 2.02E-03,1.75E-03, and 1.54E-03, respectively, compared to MVO. Comprehensive results show that the proposed method has better robustness and convergence accuracy than the state-of-the-art algorithm.