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

作　　者：孙志媛 刘默斯 周荣蓉 冀婉玉 SUN Zhiyuan;LIU Mosi;ZHOU Rongrong;JI Wanyu(Electric Power Research Institute,Guangxi Power Grid Co.,Ltd.,Nanning 530023,China;China Electric Power Research Institute Co.,Ltd.,Nanjing 210003,China)

机构地区：[1]广西电网有限责任公司电力科学研究院,南宁530023 [2]中国电力科学研究院有限公司,南京210003

出　　处：《电力需求侧管理》2025年第2期21-26,共6页Power Demand Side Management

基　　金：国家重点研发计划项目(2022YFE0129400);中国南方电网有限责任公司重点科技项目(GXKJXM20222158)。

摘　　要：由于光伏模型具有多模态和非线性的特点,其参数辨识是一个具有挑战性的问题。鉴于传统算法在光伏模型参数辨识领域所面临的可靠性不足、精度低下、易于陷入局部最优解及早熟收敛的局限,提出了一种改进型复数编码共生生物搜索算法(improved complex valued encoding symbiotic organisms search,ICSOS)用于光伏模型参数辨识。为增强传统共生生物搜索算法的寻优能力,引入了复数编码机制,由原先的一维实数编码拓展至二维复数编码空间,以扩大群体的搜索范围,增强算法的寻优能力和速度。仿真验证表明所提出的改进算法在单二极管模型和光伏组件模型参数辨识过程中均具有良好的适用性,与其他优化算法相比,ICSOS算法能够获得更低的均方根误差(root mean square error,RMSE),且能够快速寻优,有效减少预测误差,提高参数识别的精度。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 parameter 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 

分 类 号：TM615[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]