Application of Covariance Adaptive and Simplex Based Rime Algorithm in Parameter Identification of Photovoltaic Models

进入新世纪信息时代以来，全球人口和全球经济增长迅速，这也就导致了人类对能源的需求不断增加。在所有的能源获取方式中，太阳能是能源源不断获取的清洁能源，所以光伏系统模型设计的准确性和高效性对于研究人员来说更是至关重要，太阳能光伏(PV)电池建模的关键在于电池参数的精确性。准确识别太阳能光伏模型的参数对于提升光伏系统运行效率具有决定性作用。针对光伏系统参数辨识这一关键问题，本研究从群智能优化算法角度展开深入探索。本文在雾凇优化算法(RIME)的基础上，加入协方差矩阵自适应进化策略(CMA-ES)，提升算法整体解的质量，从而使种群总体向最优解区域靠近，同时引入单纯形法。该架构充分发挥RIME机制的高维空间覆盖优势，结合CMA-ES的协方差变异策略提升动态环境追踪效率，并应用在多种不同条件下提取商业光伏组件未知参数。Since entering the information age of the new century, the rapid growth of global population and economic expansion has led to an ever-increasing demand for energy. Among all energy acquisition methods, solar energy stands out as a continuously available clean energy source. Therefore, the accuracy and efficiency of photovoltaic (PV) system model design are of paramount importance to researchers. The key to modeling solar PV cells lies in the precision of cell parameters. Accurate identification of solar PV model parameters plays a decisive role in improving the operational efficiency of PV systems. To address this critical issue of parameter identification in PV systems, this study conducts an in-depth exploration from the perspective of swarm intelligence optimization algorithms. Building upon the Rime Optimization Algorithm (RIME), this paper incorporates the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to enhance the overall solution quality of the algorithm, thereby guiding the population toward the optimal solution region. Additionally, the simplex method is introduced. This framework leverages RIME’s strength in high-dimensional space coverage while combining CMA-ES’s covariance mutation strategy to improve tracking efficiency in dynamic environments. The proposed approach is applied to extract unknown parameters of commercial PV modules under various conditions.