摘要
网络拓扑辨识作为配电网运行和管理的基础,随着城市配电网结构的日趋复杂以及运行安全性和可靠性需求的日渐提高,配电网拓扑辨识面临着严峻挑战。为此,本文提出一种基于电压聚类排序的中压配电网拓扑在线辨识方法。首先梳理了配电网的典型拓扑结构,分析了配电网运行时拓扑的多样性和拓扑辨识的重要性;其次,分析了中压配电网电压的分布特征和相似性,通过挖掘节点电压相似性中蕴含的拓扑信息,利用均值漂移算法辨识分支、利用平均电压法辨识分支结构,实现对配电网拓扑结构的准确辨识;最后,采用某东部城市的负荷曲线,通过算例验证了所提拓扑辨识方法的有效性及优越性。
Network topology identification is the foundation of distribution network operation and management.With the increasing complexity of urban distribution network structure and the rising demand for operational safety and reliability,the distribution network topology identification meets a serious challenge.Accordingly,an online identification method of MV distribution network topology based on voltage clustering ranking is proposed.Firstly,the typical topology of distribution network is sorted out.The diversity of topology and the importance of topology identification are analyzed.The distribution characteristics and similarity of voltage in MV distribution network are analyzed.The topology information contained in the voltage similarity of nodes is explored.The branch is identified by mean drift algorithm,and the branch structure is identified by mean voltage method,and the accurate identification of distribution network topology is realized.Finally,the effectiveness and superiority of the proposed topology identification method are verified by an example using the load curve of an eastern city.
作者
黄飞
杨宏锐
戴健
欧阳金鑫
范昭勇
戴晖
HUANG Fei;YANG Hongrui;DAI Jian;OUYANG Jinxin;FAN Zhaoyong;DAI Hui(State Grid Chongqing Electric Power Company Electric Power Research Institute,Chongqing 401123,China;State Key Laboratory of Power Transmission and Distribution Equipment and System Safety and New Technology(Chongqing University),Chongqing 400044,China;State Grid Chongqing Electric Power Company,Chongqing 400015,China)
出处
《电工电能新技术》
CSCD
北大核心
2023年第9期77-85,共9页
Advanced Technology of Electrical Engineering and Energy
基金
国家自然科学基金联合基金项目(U1866603)
国网重庆市电力公司科技项目(SGCQDK00DWJS2100189)。
关键词
中压配电网
拓扑结构
拓扑辨识
电压相似性
均值漂移
MV distribution system
topology
topology identification
voltage similarity
mean-shift