Q-Learning-Guided Ant Colony Optimization for Resilient Fault Reconfiguration of Autonomous Shipboard Meshed Microgrids

Reliable electric-power restoration is important for autonomous ships because propulsion, navigation, communication, and emergency loads depend on a compact shipboard distribution network with limited generation redundancy. This paper studies the fault reconfiguration of an autonomous shipboard meshed microgrid under generator outage, branch fault, and dynamic-load disturbance conditions. A multi-objective model is established by considering priority-based load restoration, switching-operation cost, and generator load balancing. To represent emergency load management more realistically, a continuous restoration ratio is introduced for aggregated shipboard load groups, so that full restoration, derated operation, and load shedding can be described in one formulation. A Q-learning-guided ant colony optimization method (QL-ACO) is then proposed. In this method, Q-learning is used as an adaptive parameter controller for the pheromone factor, heuristic factor, and greedy selection probability, rather than as a direct switch-action selector. Elite reinforcement and pheromone smoothing are also introduced to reduce premature convergence. Four shipboard fault scenarios are simulated, including a single-branch fault, a single-generator outage, a combined branch–generator fault, and a dynamic-load–branch-fault case. The results show that the proposed method maintains critical-load restoration, improves Class-III load recovery in complex scenarios and obtains feasible reconfiguration schemes with fewer switching operations than fixed-parameter ACO, NSGA-II, PSO, and a compact direct RL reference baseline. Runtime, scalability, statistical, and sensitivity analyses are also provided to examine online applicability and robustness.