Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

2026-06-09 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Energy Storage & Grid Technology · Depth: Expert, extended

Summary

A novel spectral graph reinforcement learning framework has been developed for autonomous outage management in self-healing smart grids. This model employs a Spectral Graph Neural Network (GNN) within a Proximal Policy Optimization (PPO) algorithm to learn optimal power restoration policies. Crucially, it captures both local and global structural patterns by operating in the frequency domain, addressing limitations of conventional spatial GNNs in modeling system-wide interactions. The framework was evaluated on modified IEEE 13-bus, 34-bus, and 123-bus test systems, achieving near-optimal real-time performance. On the complex 123-bus network, it significantly outperformed a GCAPS-GNN baseline, supplying 1911.521 kWh compared to 983.234 kWh and reducing voltage violations from 0.5392 to 0.2576. Its millisecond-level inference time confirms suitability for real-time self-healing grid applications and demonstrates strong generalization across outage scenarios.

Key takeaway

For Machine Learning Engineers developing smart grid resilience solutions, you should consider spectral graph neural networks for outage management. This approach significantly improves power restoration and voltage regulation in complex networks like the 123-bus system, outperforming traditional spatial GNNs. Its millisecond-level inference time makes it ideal for real-time self-healing applications. You can achieve better generalization and scalability by integrating global topological information through spectral filtering in your reinforcement learning policies.

Key insights

Spectral GNNs in RL enable real-time, scalable outage management in smart grids by capturing global network topology.

Principles

• Frequency-domain GNNs capture global network structure.
• RL policies can learn optimal power restoration.
• Centralized DSO agents are practical for current DN control.

Method

A PPO-based agent uses a Spectral GNN policy network to learn optimal switching and load shedding actions, combining graph node embeddings with context information for real-time outage management.

In practice

• Apply spectral GNNs for complex network optimization.
• Use PPO for discrete, high-dimensional action spaces.
• Integrate context variables not in graph structure.

Topics

• Smart Grids
• Outage Management
• Reinforcement Learning
• Spectral Graph Neural Networks
• Power Distribution Networks
• IEEE Test Systems

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.