鉴于传统的太阳电池等效电路参数辨识方法存在结构复杂、辨识精度不高、鲁棒性不强等问题,提出一种基于改进树种算法（ITSA）的太阳电池等效电路参数辨识方法。引入随迭代次数自适应变化的搜索趋势,提升算法的局部最优收敛能力和全局搜索能力;用自适应步长因子替代算法随机步长因子,加快算法后期寻优迭代速度,缩短寻优时间。将改进的树种算法用于双二极管太阳电池等效电路模型参数辨识,与其他算法对比,该方法所得电流均方根误差最小,预测数据与测量数据拟合程度高,表明改进的树种算法能有效地对太阳电池等效电路参数进行辨识,具有较高的辨识精度和收敛性,便于工程应用。
Abstract
In response to the problems of the traditional parameter identification approaches of solar cell model, such as complex structure, low identification accuracy, weak robustness and so on, a parameter identification method for the equivalent model of solar cell based on improved tree seed algorithm （ITSA） is proposed. A variable adapting gradually with the number of iterations is introduced, which significantly enhances the capability of local optimal convergence and global searching. An adaptive step factor is introduced to replace the random step factor for improving the optimum speed and reducing the optimization time in later stages of the algorithm. Applied to the parameter identification of the double-diode model,the root mean square error of ITSA is smaller than that of other algorithms and the fitting accuracy of identification results is high with the measured data, which shows that ITSA can effectively identify the parameters of solar cell model. The algorithm has high accuraty and convergence, making it practical for engineering applications.
关键词
参数辨识 /
太阳电池 /
双二极管模型 /
改进树种算法 /
自适应步长因子
Key words
parameter identification /
solar cell /
double-diode model /
improved tree-seed algorithm /
adaptive step factor
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基金
安徽省检测技术与节能装置重点实验室开放基金（JCKJ2022A10）; 安徽省教育厅高校科研重点项目（2024AH050118）
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