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

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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Bibliographic Details
Main Authors: Yueran Liu, Peng Liao, Yang Wang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Machines
Subjects:
fault recovery
deep reinforcement learning
multiple agents
partially observable Markov decision process
distributed energy resources
Online Access:https://www.mdpi.com/2075-1702/13/7/543
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