  To enhance the intelligence degree in operation and maintenance, a novel
method for fault detection in power grids is proposed. The proposed GNN-based
approach first identifies fault nodes through a specialized feature extraction
method coupled with a knowledge graph. By incorporating temporal data, the
method leverages the status of nodes from preceding and subsequent time periods
to help current fault detection. To validate the effectiveness of the node
features, a correlation analysis of the output features from each node was
conducted. The results from experiments show that this method can accurately
locate fault nodes in simulation scenarios with a remarkable accuracy.
Additionally, the graph neural network based feature modeling allows for a
qualitative examination of how faults spread across nodes, which provides
valuable insights for analyzing fault nodes.
