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

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 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
	Liu JC(刘建昌)2; Zhang T(张彤)2 ; Sun LX(孙兰香)3,4,5 ; Yu HB(于海斌)3,4,5 ; Zhou, Xiaoming 6; Gao, Lin 7; Zhang, Yingwei 2
刊名	Electric Power Components and Systems
	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
产权排序	2
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关键词	PRINCIPAL COMPONENT ANALYSIS ; BASIS EXPANSIONS ; FUZZY-LOGIC ; CLASSIFICATION ; LINE ; ALGORITHM ; SCHEME ; MODEL
WOS研究方向	Engineering
语种	英语
WOS记录号	WOS:000458114900001
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
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/23943]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Sun LX(孙兰香)
作者单位	1.Yingkou Electric Power Supply Company, State Grid Liaoning Electric Power Supply Company Ltd, Liaoning, China 2.Institute of Automation, College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 3.Liaoning Province, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 5.Key Laboratory of Networked Control System, CAS, Shenyang, China 6.University of Chinese Academy of Sciences, Beijing, China 7.Liaoning Electric Power Compony Limited of State Grid, Shenyang, China
推荐引用方式 GB/T 7714	Liu JC,Zhang T,Sun LX,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	Liu JC.,Zhang T.,Sun LX.,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	Liu JC,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.