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.

通过图神经网络（GNN）发展了一种检测电力网故障的新方法，旨在增强网络运维中的智能故障诊断能力，该方法借助特殊的电气特征提取模型和知识图谱，通过整合节点的历史和未来状态来辅助当前故障检测，并通过对神经网络层各个节点输出特征进行相关分析验证了该 GNN 在提取节点特征方面的有效性，实验证明该方法可在模拟场景中准确定位故障节点，准确率达到 99.53%，此外，图神经网络的特征建模还可定性揭示故障在节点之间的传播情况，提供有价值的故障节点分析洞察。