基于知识嵌入型深度强化学习的电力系统频率紧急控制方法OA

The rapid development of new power systems has exacerbated frequency security challenges,making emergency control crucial for restoring stability during faults.This paper proposes a power system emergency frequency control method based on knowledge-embedded deep reinforcement learning.First,the emergency frequency control problem is formulated as a Markov decision process,with a simulation system serving as the reinforcement learning environment,and a deep reinforcement learning(DRL)agent is constructed based on the deep deterministic policy gradient(DDPG)algorithm.Furthermore,theoretical knowledge guides the action space optimization,integrating both over-frequency generator tripping and under-frequency load shedding scenarios.Finally,the proposed method is validated on the IEEE 39-bus system,demonstrating that the DRL agent can generate effective emergency frequency control strategies to ensure system security,and the knowledge-embedded technique enhances training stability and significantly improves policy learning efficiency and decision quality.