Hybrid Behavioral Cloning Based Coordinated Fault Recovery in Regional Power Distribution Network

With the high penetration of distributed energy resources (DERs) and energy storage systems, distribution network operation faces dual challenges of increased nodal overloading risks and complex dynamic characteristics. This paper proposes a Hybrid Behavioral Cloning (HBC) algorithm integrating network reconfiguration, reactive power optimization and energy storage coordination. By leveraging a policy search-guided expert knowledge imitation mechanism, a decision-making model with both rapid response capability while meeting the system operational constraints. The proposed solution adopts a dual-layer policy architecture: the upper layer employs a discrete neural network based on expert strategies for topology optimization, while the lower layer utilizes Reinforcement Learning (RL) with correlated discrete actions to achieve multi-timescale control. The solution is assessed through simulation experiments using a modified IEEE 33-bus test network with a stochastic disturbance training environment. The numerical results demonstrate that the proposed solution can recover the load under failures with a success rate of 80.3%, with a performance improvement of 56.5% and 12.5% compared with the Deep Q-Learning (DQN) algorithm and the baseline Behavioral Cloning (BC) algorithm, respectively.