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.

翻译：为提升运维智能化水平，提出一种新型电网故障检测方法。所提出的基于图神经网络的方法首先通过专用特征提取方法结合知识图谱识别故障节点。通过融入时序数据，该方法利用故障节点前后时段的状态信息辅助当前故障检测。为验证节点特征的有效性，对各节点输出特征进行了相关性分析。实验结果表明，该方法能够在仿真场景中精确定位故障节点，且准确率显著。此外，基于图神经网络的特征建模可定性分析故障在节点间的传播规律，为故障节点的分析提供了有价值的见解。