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OA

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.