SIA OpenIR  > 工业控制网络与系统研究室
Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network
Zhang T(张彤)1; Sun LX(孙兰香)2,3,4; Liu JC(刘建昌)1; Yu HB(于海斌)2,3,4; Zhou, Xiaoming5; Gao, Lin6; Zhang, Yingwei1
Department工业控制网络与系统研究室
Source PublicationElectric Power Components and Systems
ISSN1532-5008
2018
Volume46Issue:9Pages:985-996
Indexed BySCI ; EI
EI Accession number20185206299723
WOS IDWOS:000458114900001
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; IAPI Fundamental Research Funds ; National Key RD Program ; Fundamental Research Funds for the Central Universities
KeywordActive distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU)
AbstractA fault diagnosis and location method of artificial neural network (ANN) based on regularized radial basis function (RRBF) is proposed. The phase angle feature of fault voltage and current signal is analyzed. The proposed method adopts synchronized amplitude and phase angle feature for fault diagnosis based on RRBF neural network. The fault diagnosis and location for the distribution branch is researched in the IEEE 13-bus active distribution network (ADN) system. The diagnosis accuracy and location precision is analyzed considering the effect of different input signals, fault position, and fault resistance. The simulation result demonstrates that the location method based on phase angle feature shows higher accuracy. The RRBF fault diagnosis and location method aims to solve fault in ADN and lays the foundation to maintain ADN system stability.
Language英语
WOS SubjectEngineering, Electrical & Electronic
WOS KeywordPRINCIPAL COMPONENT ANALYSIS ; BASIS EXPANSIONS ; FUZZY-LOGIC ; CLASSIFICATION ; LINE ; ALGORITHM ; SCHEME ; MODEL
WOS Research AreaEngineering
Funding ProjectFundamental Research Funds for the Central Universities[N160403003] ; National Key RD Program[2017YFB0902900] ; IAPI Fundamental Research Funds[2013ZCX02-03] ; National Natural Science Foundation of China (NSFC)[61703086] ; National Natural Science Foundation of China (NSFC)[61773106] ; National Natural Science Foundation of China (NSFC)[61374137] ; National Natural Science Foundation of China (NSFC)[61374137] ; National Natural Science Foundation of China (NSFC)[61773106] ; National Natural Science Foundation of China (NSFC)[61703086] ; IAPI Fundamental Research Funds[2013ZCX02-03] ; National Key RD Program[2017YFB0902900] ; Fundamental Research Funds for the Central Universities[N160403003]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23943
Collection工业控制网络与系统研究室
Corresponding AuthorSun LX(孙兰香)
Affiliation1.Institute of Automation, College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
2.Liaoning Province, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
4.Key Laboratory of Networked Control System, CAS, Shenyang, China
5.University of Chinese Academy of Sciences, Beijing, China
6.Liaoning Electric Power Compony Limited of State Grid, Shenyang, China
7.Yingkou Electric Power Supply Company, State Grid Liaoning Electric Power Supply Company Ltd, Liaoning, China
Recommended Citation
GB/T 7714
Zhang T,Sun LX,Liu JC,et al. Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network[J]. Electric Power Components and Systems,2018,46(9):985-996.
APA Zhang T.,Sun LX.,Liu JC.,Yu HB.,Zhou, Xiaoming.,...&Zhang, Yingwei.(2018).Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network.Electric Power Components and Systems,46(9),985-996.
MLA Zhang T,et al."Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network".Electric Power Components and Systems 46.9(2018):985-996.
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