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

doi:10.1080/15325008.2018.1460884

IMR OpenIR

	Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network
	Zhang Tong 1; Sun Lanxiang 2,3,4; Liu Jianchang 1; Yu Haibin 2,3,4; Zhou Xiaoming 5; Gao Lin 6; Zhang Yingwei 1
通讯作者	Sun Lanxiang()
	2018
发表期刊	ELECTRIC POWER COMPONENTS AND SYSTEMS
ISSN	1532-5008
卷号	46 期号:9 页码:985-996
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.
关键词	Active distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU)
资助者	National Natural Science Foundation of China (NSFC) ; IAPI Fundamental Research Funds ; National Key RD Program ; Fundamental Research Funds for the Central Universities
DOI	10.1080/15325008.2018.1460884
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000458114900001
出版者	TAYLOR & FRANCIS INC
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/131699
专题	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.