中国电机工程学报2024，Vol.44Issue(5)：1675-1687,中插1,14.DOI:10.13334/j.0258-8013.pcsee.222633
基于知识融合和深度强化学习的智能紧急切机决策
Intelligent Emergency Generator Rejection Schemes Based on Knowledge Fusion and Deep Reinforcement Learning
摘要
Abstract
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.关键词
紧急切机决策/深度强化学习/决策空间/分支竞争Q网络/知识融合Key words
emergency generator rejection decision/deep reinforcement learning/decision space/branching dueling Q network/knowledge fusion分类
信息技术与安全科学引用本文复制引用
李舟平,曾令康,姚伟,胡泽,帅航,汤涌,文劲宇..基于知识融合和深度强化学习的智能紧急切机决策[J].中国电机工程学报,2024,44(5):1675-1687,中插1,14.基金项目
国家自然科学基金项目(U1866602). Project Supported by National Natural Science Foundation of China(U1866602). （U1866602）
