Using Graph-Enhanced Deep Reinforcement Learning for Distribution Network Fault Recovery

Abstract

Fault recovery in distribution networks is a complex, high-dimensional decision-making task characterized by partial observability, dynamic topology, and strong interdependencies among components. To address these challenges, this paper proposes a graph-based multi-agent deep reinforcement learning (DRL) framework for intelligent fault restoration in power distribution networks. The restoration problem is modeled as a partially observable Markov decision process (POMDP), where each agent employs graph neural networks to extract topological features and enhance environmental perception. To address the high-dimensionality of the action space, an action decomposition strategy is introduced, treating each switch operation as an independent binary classification task, which improves convergence and decision efficiency. Furthermore, a collaborative reward mechanism is designed to promote coordination among agents and optimize global restoration performance. Experiments on the PG&E 69-bus system demonstrate that the proposed method significantly outperforms existing DRL baselines. Specifically, it achieves up to 2.6% higher load recovery, up to 0.0 p.u. lower recovery cost, and full restoration in the midday scenario, with statistically significant improvements ( (Formula presented.) or (Formula presented.) ). These results highlight the effectiveness of graph-based learning and cooperative rewards in improving the resilience, efficiency, and adaptability of distribution network operations under varying conditions.