Ts-GSAN: A Two-Stage Graphical Spatiotemporal Attention Network Fault Localization Method for Distributed Energy Systems

Xuguang Hu, Jilong Ma, Rui Zhang, Dazhong Ma, Qingchen Wang

Abstract: Topological reconfiguration caused by distributed energy consumption changes the propagation path of fault features rendering localization more difficult. To address this issue, a two-stage graph spatiotemporal attention network (Ts-GSAN) fault location method is proposed for distributed energy systems. First, a low-observable node selection mechanism is proposed to realize global awareness of the system based on the least amount of data, which accounts the propagation mechanism of fault unbalanced current. Second, a graph spatiotemporal attention mechanism is proposed to solve the problem of feature propagation path change due to topology reconfiguration, which captures the feature attenuation during fault propagation. Furthermore, a two-stage graph neural network method is proposed for fault localization, which solves the problem of interference between various fault types through a localization-correction mechanism. Ts-GSAN realizes the global fault awareness of the system based on a small number of devices, which can ensure the generalizability to different topologies and the localization accuracy. Finally, experimental validation is carried out through IEEE 123 node distributed energy systems. The results show that the fault localization accuracy (FLA) reaches more than 98% with high effectiveness and feasibility.