A novel method for detecting faults in power grids using a graph neural network (GNN) has been developed, aimed at enhancing intelligent fault diagnosis in network operation and maintenance. This GNN-based approach identifies faulty nodes within the power grid through a specialized electrical feature extraction model coupled with a knowledge graph. Incorporating temporal data, the method leverages the status of nodes from preceding and subsequent time periods to aid in current fault detection. To validate the effectiveness of this GNN in extracting node features, a correlation analysis of the output features from each node within the neural network layer was conducted. The results from experiments show that this method can accurately locate fault nodes in simulated scenarios with a remarkable 99.53% accuracy. Additionally, the graph neural network's feature modeling allows for a qualitative examination of how faults spread across nodes, providing valuable insights for analyzing fault nodes.

翻译：提出了一种基于图神经网络(GNN)的电网故障检测新方法，旨在提升网络运行维护中的智能故障诊断能力。该GNN方法通过专门的电特征提取模型与知识图谱相结合，识别电网中的故障节点。该方法融合时态数据，利用节点在前、后时间步的状态信息辅助当前故障检测。为验证该GNN在提取节点特征方面的有效性，对神经网络层内各节点的输出特征进行了相关性分析。实验结果表明，该方法在模拟场景中能够以99.53%的准确率精确定位故障节点。此外，图神经网络的特征建模能力可定性分析故障在节点间的传播模式，为故障节点分析提供重要依据。