基于NSGA-II参数辨识算法的分布式光伏动态分群等值建模方法研究

熊雪君; 张雅君; 杨心刚; 潘爱强; 杨秀; 李文豪

熊雪君,张雅君,杨心刚,潘爱强,杨秀,李文豪.基于NSGA-II参数辨识算法的分布式光伏动态分群等值建模方法研究[J].电测与仪表,2026,63(4):1-10.
xiongxuejun,zhangyajun,Yang Xingang,Pan Aiqiang,Yang Xiu,Li Wenhao.Analytical research on the dynamic grouping equivalent modeling of distributed photovoltaic new energy power generation system considering parameter identification[J].Electrical Measurement & Instrumentation,2026,63(4):1-10.

基于NSGA-II参数辨识算法的分布式光伏动态分群等值建模方法研究

Analytical research on the dynamic grouping equivalent modeling of distributed photovoltaic new energy power generation system considering parameter identification

DOI：10.19753/j.issn1001-1390.2026.04.001

中文关键词: 光伏发电聚类分群等值建模改进的FCM算法 NSGA-II 主成分分析

英文关键词:photovoltaics clustering grouping equivalent modeling improved FCM algorithm NSGA-II principal component analysis

基金项目:国家电网有限公司科技项目资助（52094022004L）

作者中文名	作者英文名	单位
熊雪君	xiongxuejun	国网上海市电力公司电力科学研究院
张雅君	zhangyajun	国网上海市电力公司电力科学研究院
杨心刚	Yang Xingang	国网上海市电力公司电力科学研究院
潘爱强	Pan Aiqiang	国网上海市电力公司电力科学研究院
杨秀	Yang Xiu	上海电力大学
李文豪	Li Wenhao	上海电力大学

摘要点击次数: 6

中文摘要:

为建立大规模地区分布式的光伏系统的模型并研究其运行特性，提出一种分布式光伏发电系统动态分群多机等值建模方法。文中选择具有代表性的故障曲线的特征点，这些节点可以有效地体现出光伏系统的动态特性，然后根据改进的FCM聚类算法以选取的特征点为指标对区域内的分布式光伏系统进行聚类，然后对等值模型进行参数设计，对于难以获得的控制参数，提出一种基于NSGA-II和主成分分析的多目标优化算法来对控制参数进行辨识，其他参数根据容量加权法获得。从而构建区域分布式光伏系统多机等值模型。在此过程中，针对传统参数等值方法获取的分布式光伏多机等值模型的逆变器控制参数不够精确的问题，通过辨识手段获得等值模型的控制参数，提高了等值模型的精确性，通过Simulink仿真工具来检验所提等值建模方案是否可行且准确。

英文摘要:

To establish a model for large-scale regional distributed photovoltaic systems and study their operational characteristics, a dynamic clustering multi machine equivalent modeling method for distributed photovoltaic power generation systems is proposed. Firstly, select representative feature points of the fault curve, which can effectively reflect the dynamic characteristics of the photovoltaic system. Then, cluster the distributed photovoltaic systems in the region using the improved FCM clustering algorithm. Then, design parameters for the equivalent model. For control parameters that are difficult to obtain, a multi-objective optimization algorithm based on NSGA-II and principal component analysis is used to identify them. Other parameters are obtained using capacity weighting method. Thus constructing a multi machine equivalent model for regional distributed photovoltaic systems. In this process, in response to the problem of imprecise inverter control parameters of the distributed photovoltaic multi machine equivalent model obtained by traditional parameter equivalence methods, identification methods were used to obtain the control parameters of the equivalent model, which improved the accuracy of the equivalent model. Finally, the feasibility and accuracy of the proposed equivalent modeling scheme were verified using Simulink simulation tools.

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