A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm

Abstract

Artificial intelligence (AI)-based fault diagnosis methods have been widely studied for power grids, with most research focusing on fault interval localization rather than precise fault point identification. In cases involving long-distance transmission lines or underground cables, merely locating the fault interval is insufficient. This paper presents a novel fault diagnosis and precise localization method for power systems utilizing the Graph Sample and Aggregated (GraphSAGE) algorithm. A fault diagnosis and interval localization model are developed based on the system topology, identifying k-order adjacent nodes at both ends of the fault interval. This information is then used to construct an accurate fault point localization model. Leveraging the strong inductive learning capability of GraphSAGE, the proposed method effectively captures the impact of the fault point on surrounding nodes, enabling precise fault point localization. Experimental results demonstrate that the proposed method offers high fault diagnosis accuracy, precise localization, and robust performance. The model shows significant applicability in real-world fault scenarios, maintaining strong performance and economic value across varying network topologies and incomplete data collection.