MiniRocket-MARL synergy for storm tide resilience: MESS-DV enhanced recovery in coastal distribution networks

In coastal cities, distribution networks are vulnerable to storm tide-induced damage, which can trigger widespread power outages and economic losses if not promptly addressed. However, research addressing this challenge remains scarce. To resolve post-storm-tide fault identification and recovery issues, this study proposes a unified fault identification-recovery framework. Acknowledging the transient nature and spatial uncertainty of busbar faults during disasters, a miniRocket-based fault locator for distribution networks was developed. Validated in a digital twin system utilizing real-world grid data, it achieved 99.69 % accuracy in identifying 16 three-phase short-circuit fault locations. An uncertainty simulation environment was established to incorporate multi-system couplings in coastal cities, including storm tide risk zones, power grids, transportation networks, and urban drainage systems. By integrating the synergistic effects of active and passive drainage on low-lying areas and introducing physical-information security constraints related to water immersion depth, an event-driven multi-agent reinforcement learning (MARL) framework was designed for coordinated dispatch of mobile energy storage system (MESS) and drainage vehicle (DV) in post-disaster grid recovery. Testing demonstrated that scenarios incorporating active drainage reduced total power restoration time by approximately 6 h compared to passive-only approaches within simulation constraints. Across all test scenarios, coupled systems achieved full power restoration within 7 h, with no subsequent outages following restoration.