Intelligent Emergency Generator Rejection Schemes Based on Knowledge Fusion and Deep Reinforcement Learning
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.
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