Anomalous node localization and impact scope quantification in power system fault links using graph attention network with cascading failure propagation model

Abstract
Rapid and accurate fault localization and impact quantification are critical for preventing cascading failures in modern power grids. Traditional time-domain simulations suffer from high computational latency, while purely data-driven deep learning models lack physical interpretability and are prone to over-smoothing. This paper proposes a novel physics-informed dual-stage graph neural network (GNN) framework for real-time grid anomaly analysis. In the first stage, a degree-biased multi-head Graph Attention Network (GAT) is developed to pinpoint the fault source, effectively disentangling complex features under multiple concurrent faults. In the second stage, a Cascading Failure Propagation Model (CFPM) is explicitly embedded into the GNN’s message-passing mechanism as a physical regularization term. This physical prior forces the network to quantify the impact scope along actual power flow trajectories rather than mere topological hop counts. Extensive experiments on a hybrid dataset—comprising 15,000 simulated samples from the IEEE 118-bus system and 5,200 real-world SCADA/PMU records—demonstrate the framework’s superiority. The proposed method achieves a fault localization accuracy of 96.84%, outperforming traditional temporal models by 21%. Furthermore, the physics-informed regularization reduces the impact prediction root mean square error (RMSE) by 34% and delineates the impact boundary with an Intersection over Union (IoU) of 0.88. With an end-to-end inference time of merely 18 milliseconds on a provincial-level grid topology, the proposed framework provides a highly interpretable, ultra-low-latency solution for online grid dispatching and proactive defense.