The topology of the distribution network 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 an active distribution network based on graph deep reinforcement learning is proposed. Firstly

considering the time-varying characteristics of DG and load

a fault recovery framework for the distribution network based on the graph attention network (GAT) and the soft actor-critic (SAC) algorithm is constructed. The fault recovery method and its algorithm principle are introduced. Then

a graph 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. 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.