A multiagent deep deterministic policy gradient-based distributed protection method for distribution network

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	A multiagent deep deterministic policy gradient-based distributed protection method for distribution network
	Zeng P(曾鹏)2,3,5; Cui SJ(崔世界)2,3,4,5; Song CH(宋纯贺)2,3,5; Wang ZF(王忠锋)2,3,5; Li, Guangye 1
刊名	NEURAL COMPUTING & APPLICATIONS
	2022
页码	1-12
关键词	Distributed generation Distribution system Multiagent Power system protection Reinforcement learning
ISSN号	0941-0643
产权排序	1
英文摘要	Relay protection system plays an important role in the safe and stable operation of distribution network (DN), and the traditional model-based relay protection algorithms are difficult to solve the impact of the increasing uncertainty caused by distributed generation (DG) access on the security of DN. To solve this issue, first, the relay protection characteristics of DN under DG access are analyzed; second, the DN relay protection problem is transformed into multiagent reinforcement learning (RL) problem; third, a DN distributed protection method based on multiagent deep deterministic policy gradient (MADDPG) is proposed. The advantage of this method is that there is no need to build a DN security model in advance; therefore, it can effectively overcome the impact of uncertainty caused by DG access on DN security . Extensive experiments show the effectiveness of the proposed algorithm.
资助项目	National Key Research and Development Program of China[2018YFB1700103] ; Science and Technology Project of State Grid Zhejiang Electric Power Company Ltd[B311SX210003] ; Science and Technology Project of State Grid Liaoning Electric Power Company Ltd[2021YF-39]
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000751715200001
资助机构	National Key Research and Development Program of China [2018YFB1700103] ; Science and Technology Project of State Grid Zhejiang Electric Power Company Ltd [B311SX210003] ; Science and Technology Project of State Grid Liaoning Electric Power Company Ltd [2021YF-39]
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/30347]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Cui SJ(崔世界)
作者单位	1.State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, Liaoning, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, Liaoning, China 4.University of Chinese Academy of Sciences, Beijing 100049, China 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China
推荐引用方式 GB/T 7714	Zeng P,Cui SJ,Song CH,et al. A multiagent deep deterministic policy gradient-based distributed protection method for distribution network[J]. NEURAL COMPUTING & APPLICATIONS,2022:1-12.
APA	Zeng P,Cui SJ,Song CH,Wang ZF,&Li, Guangye.(2022).A multiagent deep deterministic policy gradient-based distributed protection method for distribution network.NEURAL COMPUTING & APPLICATIONS,1-12.
MLA	Zeng P,et al."A multiagent deep deterministic policy gradient-based distributed protection method for distribution network".NEURAL COMPUTING & APPLICATIONS (2022):1-12.