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Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network

文献类型：期刊论文

作者Zhang Tong1; Sun Lanxiang2,3,4; Liu Jianchang1; Yu Haibin2,3,4; Zhou Xiaoming5; Gao Lin6; Zhang Yingwei1
刊名ELECTRIC POWER COMPONENTS AND SYSTEMS
出版日期2018
卷号46期号:9页码:985-996
关键词Active distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU)
ISSN号1532-5008
DOI10.1080/15325008.2018.1460884
通讯作者Sun Lanxiang()
英文摘要A 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.
资助项目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]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000458114900001
出版者TAYLOR & FRANCIS INC
资助机构National Natural Science Foundation of China (NSFC) ; IAPI Fundamental Research Funds ; National Key RD Program ; Fundamental Research Funds for the Central Universities
源URL[http://ir.imr.ac.cn/handle/321006/131698]  
专题金属研究所_中国科学院金属研究所
通讯作者Sun Lanxiang
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：金属研究所

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