Adaptive Emergency Control of Power Systems Based on Deep Belief Network

扫码查看

原文链接

维普
万方数据

外文摘要：Emergency control is an essential means to help system maintain synchronism after fault clearance.Traditional"offline calculation,online matching"scheme faces significant challenges on adaptiveness and robustness problems.To ad-dress these challenges,this paper proposes a novel closed-loop framework of transient stability prediction(TSP)and emergency control based on Deep Belief Network(DBN).First,a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted,which takes into account accuracy and rapidity at the same time.Next,a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity.When impending instability of the system is foreseen,optimal emergency control strategy can be determined in time.Lastly,responses after emergency control are fed back to the TSP model.If prediction result is still unstable,an additional control strategy will be implemented.Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council(NPCC)140-bus system.Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.

外文关键词：

Deep learningemergency controlpower systemsensitivitytransient stability prediction

作者：

Junyong Wu、Baoqin Li、Liangliang Hao、Fashun Shi、Pengjie Zhao

展开 >

作者单位：

Department of Electrical Engineering,Beijing Jiaotong University,Beijing 100084,China

基金：

Fundamental Research Funds for the Central UniversitiesNational Key R&D Program of ChinaScience and Technology Projects of State Grid Corporation of China

项目编号：

2020YJS1622018YFB0904500SGLNDK00KJJS1800236

出版年：

2024

DOI：

10.17775/CSEEJPES.2022.00070

中国电机工程学会电力与能源系统学报(英文版)

中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI

ISSN：2096-0042

年,卷(期)：2024.10(4)