针对传统建模与参数辨识方法难以满足高精度表征与预测需求的问题,提出一种用于解决太阳电池模型参数辨识问题的改进红嘴蓝鹊优化算法（IRBMO）。首先,采用一种新型融合反向学习策略的Sobol序列进行种群初始化,增加红嘴蓝鹊种群的多样性,可有效规避算法早熟收敛问题;其次,通过正余弦函数“振荡”与缩放调控策略及带有随机项的非线性收敛因子更新策略的引入,进一步提高算法的收敛速度和收敛精度,优化个体搜索路径以达到有效平衡算法的全局搜索和局部探索的目的。研究结果表明：相较于其他算法,IRBMO算法在光伏参数估计精度方面展现出显著优势,求得太阳电池模型的电流均方根误差为9.8602×10-4。在不同太阳辐照度条件下,IRBMO算法的辨识结果与实际曲线拟合度高,能对太阳电池模型参数进行准确有效辨识。
Abstract
To address the issue that traditional modeling and parameter identification methods fail to meet the requirements of high-precision characterization and prediction, this study proposes an Improved Red-billed Blue Magpie Optimization （IRBMO） algorithm for solar cell model parameter identification. Firstly, a novel population initialization strategy employing Sobol sequences integrated with opposition-based learning enhances population diversity while effectively mitigating premature convergence. Secondly, the implementation of a sine-cosine oscillation modulation and scaling control mechanism and a nonlinear convergence factor update strategy with stochastic components optimizes search trajectories, achieving superior balance between global exploration and local exploitation capabilities. In summary, experimental results demonstrate that the IRBMO algorithm outperforms other state-of-the-art algorithms in photovoltaic parameter estimation accuracy, achieving a root mean square current error of 9.8602E-04 for solar cell models. Under varying solar irradiance conditions, the IRBMO algorithm demonstrates a close agreement between the identified results and the measured curves, enabling accurate and effective identification of solar cell model parameters.
关键词
太阳电池 /
参数辨识 /
优化 /
Sobol序列 /
反向学习 /
正余弦 /
收敛因子 /
IRBMO
Key words
solar cells /
parameter identification /
optimization /
Sobol sequence /
opposition-based learning /
sine and cosine /
convergence factor /
IRBMO
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基金
国家自然科学基金青年科学基金（62403329）; 辽宁省兴辽英才计划（XLYC2008005）
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