电力系统保护与控制2009，Vol.37Issue(14)：122-128,7.
强化学习理论在电力系统中的应用及展望
Application and development of reinforcement learning theory in power systems
摘要
Abstract
Reinforcement Learning (RL) theory is an important branch of the machine learning in the field of artificial intelligence, which is also the general method to deal with Markov Decision Process problems. RL takes learning as trial and error process so as to maximize the reward value function by choosing an action depending on the state. In recent years, RL and its application are received increasing attention of international academia. In order to propel the further study on the aspect of RL in power systems, this paper introduces the basic idea and algorithms systematically, the main achievements of RL are surveyed in security and stability control, automatic generation control, voltage and reactive power control and electricity market respectively. Furthermore, the paper discusses the application potentials of RL in power system operation and control, and the combination of RL with classical control, ANN, fuzzy theory and multi-agent system. Meanwhile, the prospect of RL theory in power system is brought forward.关键词
人工智能/强化学习/马尔可夫决策过程/随机最优控制/电力系统Key words
artificial intelligence/reinforcement learning/Markov Decision process/stochastic optimal control/power system分类
信息技术与安全科学引用本文复制引用
余涛,周斌,甄卫国..强化学习理论在电力系统中的应用及展望[J].电力系统保护与控制,2009,37(14):122-128,7.基金项目
国家自然科学基金项目(50807016) （50807016）
广东省自然科学基金博士启动基金项目(06300091)This work is jointly supported by National Natural Science Foundation of China(No.50807016) and Guangdong Natural Science Funds Project (No. 06300091). （06300091）
