高电压技术2023，Vol.49Issue(12)：5171-5186,16.DOI:10.13336/j.1003-6520.hve.20230350
基于深度强化学习的电力系统暂态稳定控制策略研究综述
Review of Power System Transient Stability Control Strategies Based on Deep Reinforcement Learning
摘要
Abstract
Under the carbon peak and neutrality targets,the large-scale grid connection of renewable energy and the op-eration of high proportion of power electronic equipment have reduced the inertia of the system and impacted the stable operation of the power system.Traditional transient stability analysis has some shortcomings,such as difficulty in model-ing,low calculation efficiency,and being easy to be disturbed by uncertain factors.In recent years,reinforcement learning has developed rapidly.Deep reinforcement learning combines the advantages of deep learning and reinforcement learning,and it can learn a large number of high-dimensional and uncertain data to solve decision-making problems in large-scale scenes with limited information.This paper first summarizes deep reinforcement learning.Next,the existing research re-sults of reinforcement learning in power system transient stability control decision-making are summarized.Then,this paper analyzes the research status and advantages of deep reinforcement learning algorithm in power system transient sta-bility control decision-making from three aspects of system preventive control,system emergency control,and system recovery control,and the existing problems in this research direction are discussed in depth.Finally,the prospects in fu-ture technical developments and practical applications of deep reinforcement learning are put forward.关键词
强化学习/深度强化学习/暂态稳定/稳定控制决策/预防控制/紧急控制/恢复控制Key words
reinforcement learning/deep reinforcement learning/transient stability/stable control decisions/preventive control/emergency control/recovery control引用本文复制引用
江昌旭,刘晨曦,林铮,林俊杰..基于深度强化学习的电力系统暂态稳定控制策略研究综述[J].高电压技术,2023,49(12):5171-5186,16.基金项目
福建省自然科学基金(2022J05125 （）
2021J05134).Project supported by Natural Science Foundation of Fujian Province(2022J05125,2021J05134). （2022J05125,2021J05134）
