中国电机工程学报2026，Vol.46Issue(11)：4449-4466,中插7,19.DOI:10.13334/j.0258-8013.pcsee.251370
基于物理一致性动态时空图神经网络的配电网拓扑检测与状态估计
Distribution System Topology Detection and State Estimation Using a Physics-consistent Dynamic Spatiotemporal Graph Neural Network
摘要
Abstract
State estimation is the foundation of situational awareness in distribution systems and also a prerequisite for secure,reliable,and efficient operation.However,timely acquisition of the actual operating topology is hindered by limited measurement coverage and the lack of switch status information in real time.Meanwhile,as distributed energy resources and flexible loads continue to penetrate,increasingly diverse operating modes are observed,and topological uncertainty is further exacerbated by frequent switching,posing severe challenges to state estimation.To address these issues,this paper proposes a dynamic spatiotemporal graph neural network method with physical consistency for topology detection and state estimation.A design with a single encoder and multiple decoders is adopted,under which spatiotemporal features from multiple sources are extracted by a shared encoder and topology detection results and system states are jointly produced by the decoders.An adaptive dynamic graph mechanism is introduced,by which electrical correlations among nodes are inferred from measurements in real time,thereby addressing the difficulty of constructing a dynamic graph when only part of the topology is observable.In addition,power flow equations are embedded in the loss as physical consistency constraints.As a result,interpretability is improved and robustness across operating conditions is enhanced.As shown by simulation studies,high accuracy is maintained under sparse measurements and strong noise,and comparable accuracy is sustained for topologies unseen during training without transfer learning.Generalization across scenarios is thereby demonstrated,and a physically interpretable,dynamically adaptive solution for topology detection and state estimation is also provided.关键词
配电网状态估计/拓扑检测/时空信息融合/动态时空图神经网络/多任务学习/物理一致性/物理信息融合Key words
distribution system state estimation/topology detection/spatiotemporal information fusion/dynamic spatiotemporal graph neural network/multi-task learning/physical consistency/physics-informed information fusion分类
信息技术与安全科学引用本文复制引用
韩子昂,陈春,曹一家,王利利,李勇,孙辰昊..基于物理一致性动态时空图神经网络的配电网拓扑检测与状态估计[J].中国电机工程学报,2026,46(11):4449-4466,中插7,19.基金项目
湖南省自然科学基金优秀青年项目(2023JJ20039) （2023JJ20039）
国家自然科学基金项目(52007009).Project Supported by Natural Science Foundation for Excellent Youth of Hunan Province(2023JJ20039) （52007009）
Project Supported by National Natural Science Foundation of China(52007009). （52007009）
