Using Graph-Enhanced Deep Reinforcement Learning for Distribution Network Fault Recovery
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/7/543 |
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| Summary: | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></semantics></math></inline-formula> or <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.01</mn></mrow></semantics></math></inline-formula>). 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. |
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| ISSN: | 2075-1702 |