Fault location estimation of power grid using graph attention networks

The reliability of power grids is of paramount importance to modern infrastructure. Precise fault location estimation is crucial for the efficient operation and rapid recovery of electrical networks following outages. This paper presents a novel approach using Graph Attention Networks (GAT) to improve fault location estimation within power grids. Leveraging two years of real-world data from a power grid's monitoring system, encompassing 200 fault instances, our model demonstrates a significant advancement over traditional methods. The GAT model capitalizes on an attention-driven mechanism, providing a dynamic and focused analysis of the grid's topological data, which enhances the accuracy of fault detection. Comparative experiments show that GAT model outperforms benchmark algorithms, Graph Convolutional Networks (GCN), and Graph Neural Networks (GNN), with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The results highlight the GAT's potential as a robust and reliable tool for fault diagnosis in power grids, promising substantial improvements in operational resilience and maintenance efficiency.