电力需求侧管理2025，Vol.27Issue(2)：21-26,6.DOI:10.3969/j.issn.1009-1831.2025.02.004
基于改进复数编码共生生物搜索算法的光伏模型参数辨识
Parameter identification of photovoltaic models based on improved complex valued encoding SOS algorithm
摘要
Abstract
Due to the multimodal and nonlinear nature of photovoltaic(PV)models,parameter identification is a challenging problem.In view of the limitations faced by traditional algorithms in the field of PV model parameter identification,such as insufficient reliability,low accuracy,easy to fall into local optimal solutions and premature convergence,a improved complex valued encoding symbiotic organisms search(ICSOS)is proposed for PV model parameter identification.In order to enhance the optimization ability of the traditional symbiotic organism search algorithm,a complex valued encoding is introduced,which expands the original one-dimensional real number coding to a two-dimensional complex coding space,in order to expand the search range of the population and enhance the optimization ability and speed of the algorithm.Simulation validation shows that the proposed improved algorithm has good applicability in the process of parame-ter identification in single diode model,and PV module model,and compared with other optimization algorithms,the ICSOS algorithm is able to obtain lower root mean square error(RMSE)values and can quickly find the optimum to effectively reduce the prediction error and improve the accuracy of parameter identification.关键词
光伏模型/参数辨识/复数编码/改进型共生生物搜索算法Key words
photovoltaic models/parameter identification/complex valued encoding/improved symbiotic organisms search algorithm分类
动力与电气工程引用本文复制引用
孙志媛,刘默斯,周荣蓉,冀婉玉..基于改进复数编码共生生物搜索算法的光伏模型参数辨识[J].电力需求侧管理,2025,27(2):21-26,6.基金项目
国家重点研发计划项目(2022YFE0129400) （2022YFE0129400）
中国南方电网有限责任公司重点科技项目(GXKJXM20222158) （GXKJXM20222158）
