用多层图神经网络提升电网故障预测与风险分组，效果优于传统方法。
Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
- 融合空间、时间、因果三类图网络，通过注意力加权整合特征。
- 30天预测F1达0.8935，比单层GNN高10%-15%，因果模块不可或缺。
- 可识别8类风险集群，适合电网运维与韧性评估人员参考。
美国因停电造成的经济损失每年超1500亿美元，部分原因在于现有预测性维护（PdM）模型忽视了电网故障中的空间、时间和因果依赖。本研究提出一种多层图神经网络（GNN）框架，用于提升电网故障预测并实现基于韧性的变电站聚类。基于俄克拉荷马天然气与电力公司七年的事故数据（共292,830条记录，覆盖347个变电站），该框架整合了图注意力网络（空间）、图卷积网络（时间）和图同构网络（因果），并通过注意力加权嵌入进行融合。模型在30天预测中取得F1分数0.8935 ± 0.0258，优于XGBoost和随机森林3.2%和2.7%，也比单层GNN高出10%至15%。移除因果模块后性能降至0.7354 ± 0.0418。在韧性分析方面，基于HierarchicalRiskGNN嵌入使用HDBSCAN聚类识别出8个运行风险组。最高风险组（第5类，44个变电站）年均故障388.4次，恢复时间602.6分钟；低风险组年故障少于62次。ANOVA检验（p < 0.0001）证实组间差异显著。该聚类方法优于K-Means和谱聚类，轮廓系数0.626，戴维斯-布土因指数0.527。本研究支持通过精准预测与风险感知聚类实现主动电网管理。
原文摘要 · Abstract (English)
Unplanned power outages cost the US economy over $150 billion annually, partly due to predictive maintenance (PdM) models that overlook spatial, temporal, and causal dependencies in grid failures. This study introduces a multilayer Graph Neural Network (GNN) framework to enhance PdM and enable resilience-based substation clustering. Using seven years of incident data from Oklahoma Gas & Electric (292,830 records across 347 substations), the framework integrates Graph Attention Networks (spatial), Graph Convolutional Networks (temporal), and Graph Isomorphism Networks (causal), fused through attention-weighted embeddings. Our model achieves a 30-day F1-score of 0.8935 +/- 0.0258, outperforming XGBoost and Random Forest by 3.2% and 2.7%, and single-layer GNNs by 10 to 15 percent. Removing the causal layer drops performance to 0.7354 +/- 0.0418. For resilience analysis, HDBSCAN clustering on HierarchicalRiskGNN embeddings identifies eight operational risk groups. The highest-risk cluster (Cluster 5, 44 substations) shows 388.4 incidents/year and 602.6-minute recovery time, while low-risk groups report fewer than 62 incidents/year. ANOVA (p < 0.0001) confirms significant inter-cluster separation. Our clustering outperforms K-Means and Spectral Clustering with a Silhouette Score of 0.626 and Davies-Bouldin index of 0.527. This work supports proactive grid management through improved failure prediction and risk-aware substation clustering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。