基于知识融合和深度强化学习的智能紧急切机决策OA北大核心CSTPCD

Emergency control is an important means of maintaining power system transient security and stability following serious faults.The current popular"human-in-the-loop"offline emergency control decision-making method has some drawbacks,including low efficiency and heavy reliance on expert experience.Therefore,this paper proposes an intelligent emergency generator rejection decision-making method based on knowledge fusion and deep reinforcement learning(DRL).First,a DRL-based emergency generator rejection decision-making framework is built.Then,when the agent deals with multi-generator decisions,the resulting high-dimensional decision space makes the agent training difficult.There are two solutions proposed:decision space compression and the application of a branching dueling Q(BDQ)network.Next,to further improve the exploration efficiency and the decision-making quality of the agent,the knowledge and experience related to emergency generator rejection control are integrated to the agent training.Finally,the simulation results in the 10-machine 39-bus system show that the proposed method can quickly give effective emergency generator rejection decisions in multi-generator decision-making.Applying a BDQ network has better decision performance than decision space compression.The knowledge fusion strategy can guide the agents to reduce ineffective decision-making explorations and improve decision-making performance.