配电网拓扑辨识研究综述及展望

doi:10.13229/j.cnki.jdxbgxb20220622

Abstract

Abstract:

Topology identification of distribution network is an important work to ensure the safe and stable operation of distribution network. It could provide structure data for system power flow calculation， load capacity distribution， fault diagnosis， power network state estimation， which is the foundation of distribution network system analysis.The existing research of distribution network topology identification into two categories could be divided in this paper ： the first type of method is based on historical topology information， including matrix method， innovation graph method and optimal matching method. The second type of method is based on real-time measurement information， including correlation judgment method， signal injection method， linear programming method and machine learning method. Finally，the application range， main used data and characteristics of the existing methods was analyzed，the future research direction of distribution network topology identification was proposed.

Key words: power system, distribution network, topology identification, historical topology information, real-time measurement information

CLC Number:

TM71

Guo WANG,Wen-kai GUO,Chang-chun WANG. Overview and prospect of distribution network topology identification[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 312-327.

Figures/Tables 7

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方法	适用结构	主要使用数据	特点
矩阵法	不限结构	节点与连边信息	能通过拓扑图直观反映出连接关系，但面对复杂网络架构时，矩阵编写繁琐，工作量大
新息图法	辐射状结构	节点与连边信息、潮流参数	可同时进行故障定位和异常数据检测，但依赖于状态估计结果，对历史数据质量有较高要求
最优匹配法	不限结构	潮流参数	可对某条支路或回路进行精准辨识，但依赖于状态估计结果，对历史数据质量有较高要求
相关性判断法	辐射状结构	潮流参数	原理简单，计算步骤少，但主要适用于单台区拓扑辨识，所需数据量较大
信号注入法	辐射状结构	电力载波信号、潮流参数	无需计算，通过信号分析完成拓扑辨识，但主要适用于单个台区拓扑辨识，且需多种设备支撑
线性规划法	不限结构	潮流参数、开关状态	原理简单，可利用数据较多，但计算复杂，拓扑辨识精度较低
机器学习法	不限结构	不限数据	无需计算，通过模型训练完成拓扑辨识，但易受数据质量和训练环境等外界因素影响

Overview and prospect of distribution network topology identification

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