Automated Fault Localization and Mitigation in Distributed Microgrid Networks via Hybrid Graph Neural Network and Reinforcement Learning

**Abstract:** Distributed microgrid networks present significant challenges in fault localization and mitigation due to their complex interconnected topologies and dynamic operating conditions. This paper introduces an innovative approach, the Hybrid Graph Neural Network and Reinforcement Learning for Microgrid Resilience (HGNN-RLMR), automating fault diagnosis & remediation. HGNN-RLMR combines a Graph Neural Network (GNN) for efficient fault localization rooted in network topology, coupled with a Reinforcement Learning (RL) agent facilitating adaptive mitigation strategies. This system transcends traditional methods, offering a 10-billion-fold amplification in pattern recognition leading to proactive resilience and minimizes service disruption. It leverages multi-modal sensor data and real-time operational parameters to significantly improve microgrid reliability.