Research on Distribution Network Fault Handling Optimization Algorithm Based on Reinforcement Learning

This paper proposes a distribution network fault handling optimization algorithm based on reinforcement learning, which realizes intelligent handling of distribution network faults through the deep Q network (DQN) model. The algorithm models the distribution network fault handling problem as a reinforcement learning problem, designs a state space containing information such as voltage and current, an action space of different fault handling methods, and a composite reward function that comprehensively considers factors such as fault recovery time, system stability and maintenance cost. Innovatively combining the multi-objective optimization idea, the fault recovery process is optimized through reinforcement learning strategy. This study constructs an experimental platform based on the distribution network fault dataset, simulates a variety of common fault types such as short circuit and overload, and compares it with traditional algorithms. Experimental results show that the proposed reinforcement learning algorithm can significantly reduce the fault handling time and improve the recovery speed and system stability of the power grid. In 500 simulation tests, the fault recovery time was reduced by 22% on average, the system stability was improved by 15%, and the algorithm has good robustness and can cope with changes in different fault types and environments.