A Graph Deep Reinforcement Learning-Based Fault Restoration Method for Active Distribution Networks

The topology of distribution networks changes frequently, and the uncertainty of load level and distributed generator (DG) output makes the operation scenarios more complex and variable. Based on this, a fault recovery method for active distribution networks based on graph-based deep reinforcement learning is proposed. Firstly, considering the time-varying characteristics of DG output and load, a fault recovery framework for distribution networks based on a graph attention network (GAT) and soft actor–critic (SAC) algorithm is constructed, and the fault recovery method and its algorithm principle are introduced. Then, a graph-based deep reinforcement learning model for distribution network fault recovery is established. By embedding GAT into the pre-neural network of the SAC algorithm, the agent’s perception ability of the distribution network operation status and topology is improved, and an invalid action masking mechanism is innovatively introduced to avoid illegal actions. Through the interaction between the agent and the environment, the optimal switch action control strategy is found to realize the optimal learning of recovery under high DG penetration. Finally, the proposed method is verified on IEEE 33-bus and 148-bus examples and, compared with multiple baseline methods, the proposed method can achieve the fastest fault recovery at the millisecond level, and has a more efficient and superior recovery effect; the load supply rate under topology change increased by 4% to 5% compared with the benchmark model.