PIDGAF-Net: A physical-information dual-graph adaptive fusion network for distribution network fault location

Abstract

Accurate and robust fault location is of great importance for improving the power supply reliability and automation level of modern distribution networks. However, existing methods often rely on line parameters or signal features and fail to fully exploit physical topology priors and dynamic fault-response relationships. To address this issue, this paper proposes a Physical-Information Dual-Graph Adaptive Fusion Network (PIDGAF-Net) for distribution network fault location. The proposed method constructs a static physical graph to encode feeder topology and electrical coupling, and a dynamic information graph to characterize sample-specific node response similarity under fault conditions. A dual-branch graph attention encoder and a node-level gating mechanism are then employed to adaptively fuse the two graph representations, enabling more discriminative fault feature learning. Experimental results on the IEEE 33-bus distribution system show that PIDGAF-Net achieves the highest localization accuracy of 95.87%, while maintaining moderate computation time, parameter scale, and memory overhead. In addition, the proposed method remains stable under inaccurate measurement, asynchronous sampling, data loss, and mixed anomaly conditions. These results demonstrate its strong potential for intelligent fault diagnosis and operation support in distribution networks.