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
李舟平 1曾令康 1姚伟 1胡泽 1帅航 2汤涌 3文劲宇1
作者信息
- 1. 强电磁工程与新技术国家重点实验室(华中科技大学电气与电子工程学院),湖北省 武汉市 430074
- 2. 田纳西大学电气工程与计算机科学系,美国 田纳西州 诺克斯维尔市 37996
- 3. 中国电力科学研究院有限公司,北京市 海淀区 100192
- 折叠
摘要
紧急控制是在严重故障后维持电力系统暂态安全稳定的重要手段.目前常用的"人在环路"离线紧急控制决策制定方式存在效率不高、严重依赖专家经验等问题,该文提出一种基于知识融合和深度强化学习(deep reinforcement learning,DRL)的智能紧急切机决策制定方法.首先,构建基于DRL的紧急切机决策制定框架.然后,在智能体处理多个发电机决策时,由于产生的高维决策空间使得智能体训练困难,提出决策空间压缩和应用分支竞争 Q(branching dueling Q,BDQ)网络的两种解决方法.接着,为了进一步提高智能体的探索效率和决策质量,在智能体训练中融合紧急切机控制相关知识经验.最后,在10机39 节点系统中的仿真结果表明,所提方法可以在多发电机决策时快速给出有效的紧急切机决策,应用BDQ网络比决策空间压缩的决策性能更好,知识融合策略可引导智能体减少无效决策探索从而提升决策性能.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据