Abstract
Identifying the location of faults, anomalies, and failures is a long-standing but critical challenge in power system networks. The implementation of smart meters and advanced sensor measurement technologies in recent years has allowed power systems operators to more accurately identify fault locations, quickly resolve problems, and improve the overall reliability of power networks. However, the presence of complex topology changes, penetration of renewable energy resources, stochastic propagation of anomalies in the network, and missing data require the use of new approaches that are sensitive to these issues. This article introduces an innovative method that uses a graph convolutional network (GCN) combined with a modified probability propagation matrix and dual graphs to identify and locate node/line anomalies using network sensors installed on both nodes and lines. Also, an optimization model was developed to find the most likely sources of anomalies across all nodes and edges of the network. The proposed method, which was evaluated on the IEEE 118-bus system and a set of simulated data, demonstrated outstanding performance in handling complex topologies and missing data. Although the proposed model is designed for power networks, its flexible characteristics make it applicable to many sensor-intensive networks or graph structures (e.g., transportation and social networks) where anomaly detection at nodes and/or edges is critical.