Multilayer GNN for Predictive Maintenance and Clustering in Power Grids

• URL: http://arxiv.org/abs/2507.07298v1
• Date: Wed, 09 Jul 2025 21:44:51 GMT
• Title: Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
• Abstract summary: Unplanned power outages cost the US economy over $150 billion annually.<n>This study introduces a multilayer Graph Neural Network framework to enhance PdM and enable resilience-based substation clustering.
• Score: 14.557868383315022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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