中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A multiagent deep deterministic policy gradient-based distributed protection method for distribution network

文献类型:期刊论文

作者Zeng P(曾鹏)1,3,4; Cui SJ(崔世界)1,2,3,4; Song CH(宋纯贺)1,3,4; Wang ZF(王忠锋)1,3,4; Li, Guangye5
刊名NEURAL COMPUTING & APPLICATIONS
出版日期2022
页码1-12
ISSN号0941-0643
关键词Distributed generation Distribution system Multiagent Power system protection Reinforcement learning
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]
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 Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, Liaoning, China
4.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China
5.State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, 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.

入库方式: OAI收割

来源:沈阳自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。