Reactive Power Optimization Using Feed Forward Neural Deep Reinforcement Learning Method : (Deep Reinforcement Learning DQN algorithm)

This paper focuses on reactive power optimization based on deep reinforcement learning. A deep reinforcement learning algorithm DQN is selected as an improvement of the reinforcement learning algorithm, this algorithm uses a deep neural network instead of a Q value table in the reinforcement learning algorithm, the method of power flow calculation in the Matlab toolbox is executed to verify the optimization effect. The single-section training results of the 14-BUS IEEE power grid model are tested and the parameters of the power grid model are modified after the optimization is confirmed. The DQN model is trained in a simulated complex environment, and the performance of the optimization effect is tested after the training is completed. Reactive power optimization of the power grid based on deep reinforcement learning is validated by experimental results.