Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis

doi:10.1080/02533839.2018.1490204

IMR OpenIR

	Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis
	Zhang, Tong 1; Liu, Jianchang 1; Sun, Lanxiang 2,3,4; Yu, Haibin 2,3,4; Zhang, Yingwei 1
通讯作者	Liu, Jianchang(liujianchang@ise.neu.edu.cn)
	2018
发表期刊	JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
ISSN	0253-3839
卷号	41 期号:5 页码:375-386
摘要	The improved radial basis function (RBF) method utilizes an orthogonal regression matrix to produce an artificial neural network structure based on regularized least square. The phase angle and amplitude signal of fault voltage and current are extracted based on frequency domain analysis. The proposed method adopts the fault signal for fault diagnosis synchronously. The IEEE 13-bus active distribution network (ADN) simulation model is set up in Matlab. Test results demonstrate that accuracy of the fault diagnosis can reach 98.07% and the response time of the fault classification method is less than 0.04s. The wavelet neural network (WNN) model is developed to extract the maximum decomposition level and time series behavior. The WNN method can resist noise effects and improve the fault classification accuracy by 4.3%. The effect of fault type and fault resistance on the fault location method is researched. The fault simulation result shows that the proposed method can locate a fault precisely and synchronously. The improved RBF method can diagnose the fault section, classify the fault type and locate a fault accurately in ADN. The research is significant to maintain system stability against realistic fault and network restore.
关键词	ANN neural network phase angle active distribution network (ADN) fault diagnosis
资助者	National Natural Science Foundation of China ; National High Technology Research and Development Program of China ; IAPI Fundamental Research Funds
DOI	10.1080/02533839.2018.1490204
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61374137] ; National Natural Science Foundation of China[61100159] ; National Natural Science Foundation of China[61233007] ; National High Technology Research and Development Program of China[2011AA040103] ; IAPI Fundamental Research Funds[2013ZCX02-03]
WOS研究方向	Engineering
WOS类目	Engineering, Multidisciplinary
WOS记录号	WOS:000443901100002
出版者	CHINESE INST ENGINEERS
引用统计	被引频次：3[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/321006/129405
专题	中国科学院金属研究所
通讯作者	Liu, Jianchang
作者单位	1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110000, 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
推荐引用方式 GB/T 7714	Zhang, Tong,Liu, Jianchang,Sun, Lanxiang,et al. Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis[J]. JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS,2018,41(5):375-386.
APA	Zhang, Tong,Liu, Jianchang,Sun, Lanxiang,Yu, Haibin,&Zhang, Yingwei.(2018).Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis.JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS,41(5),375-386.
MLA	Zhang, Tong,et al."Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis".JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 41.5(2018):375-386.