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.
XIONG Xuejun,ZHANG Yajun,YANG Xingang,PAN Aiqiang,YANG Xiu,LI Wenhao.Research on the equivalent modeling method of distributed photovoltaic dynamic grouping based on NSGA-II parameter identification algorithm[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

Research on the equivalent modeling method of distributed photovoltaic dynamic grouping based on NSGA-II parameter identification algorithm

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:photovoltaic, 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	XIONG Xuejun	国网上海市电力公司电力科学研究院，上海 200437
张雅君	ZHANG Yajun	国网上海市电力公司电力科学研究院，上海 200437
杨心刚	YANG Xingang	国网上海市电力公司电力科学研究院，上海 200437
潘爱强	PAN Aiqiang	国网上海市电力公司电力科学研究院，上海 200437
杨秀	YANG Xiu	上海电力大学，上海 200090
李文豪	LI Wenhao	上海电力大学，上海 200090

摘要点击次数: 683

中文摘要:

随着分布式光伏渗透率不断提高，传统的光伏建模方法已无法满足建模需求。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作为聚类指标，然后根据改进的FCM（fuzzy-C means）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，提出一种基于非支配性排序遗传算法II（non-dominated sorting genetic algorithm II, 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。通过Simulink仿真工具验证了所提等值建模方案能够明显地提高分布式光伏等值模型的准确性。

英文摘要:

With the continuous increase in the penetration rate of distributed photovoltaics, traditional photovoltaic modeling methods are no longer able to meet modeling needs. 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, representative feature points of the fault curve are selected as clustering indicators. Then, based on the improved FCM (fuzzy-C means) clustering algorithm, the distributed photovoltaic systems in the region are clustered using the selected feature points as indicators. Subsequently, the parameters design of the equivalent model is carried out. For control parameters that are difficult to obtain, a multi-objective optimization algorithm based on NSGA-II (non-dominated sorting genetic algorithm II) and principal component analysis is used to identify them. Other parameters are obtained using capacity weighting method. Thus a multi-machine equivalent model for regional distributed photovoltaic systems is constructed. Finally, the feasibility and accuracy of the proposed equivalent modeling scheme of distributed photovoltaic equivalent model is significantly improved using Simulink simulation tools.

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