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Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network
Zhang Tong1; Sun Lanxiang2,3,4; Liu Jianchang1; Yu Haibin2,3,4; Zhou Xiaoming5; Gao Lin6; Zhang Yingwei1
Corresponding AuthorSun Lanxiang()
2018
Source PublicationELECTRIC POWER COMPONENTS AND SYSTEMS
ISSN1532-5008
Volume46Issue:9Pages:985-996
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.
KeywordActive distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU)
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; IAPI Fundamental Research Funds ; National Key RD Program ; Fundamental Research Funds for the Central Universities
DOI10.1080/15325008.2018.1460884
Indexed BySCI
Language英语
Funding ProjectNational 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]
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000458114900001
PublisherTAYLOR & FRANCIS INC
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/131699
Collection中国科学院金属研究所
Corresponding AuthorSun Lanxiang
Affiliation1.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Coll Informat Sci & Engn, Inst Automat, Shenyang, Liaoning, Peoples R China
2.Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Liaoning, Peoples R China
3.Chinese Acad Sci, Key Lab Networked Control Syst, Shenyang, Liaoning, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Liaoning Elect Power Compony Ltd State Grid, Shenyang, Liaoning, Peoples R China
6.State Grid Liaoning Elect Power Supply Co Ltd, Yingkou Elect Power Supply Co, Shenyang, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Zhang Tong,Sun Lanxiang,Liu Jianchang,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 Tong.,Sun Lanxiang.,Liu Jianchang.,Yu Haibin.,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 Tong,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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