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

文献类型：期刊论文

作者Zhang, Tong4; Liu, Jianchang4; Sun, Lanxiang1,2,3; Yu, Haibin1,2,3; Zhang, Yingwei4
刊名JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
出版日期2018
卷号41期号:5页码:375-386
关键词ANN neural network phase angle active distribution network (ADN) fault diagnosis
ISSN号0253-3839
DOI10.1080/02533839.2018.1490204
通讯作者Liu, Jianchang(liujianchang@ise.neu.edu.cn)
英文摘要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.
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
语种英语
WOS记录号WOS:000443901100002
出版者CHINESE INST ENGINEERS
资助机构National Natural Science Foundation of China ; National High Technology Research and Development Program of China ; IAPI Fundamental Research Funds
源URL[http://ir.imr.ac.cn/handle/321006/129404]  
专题金属研究所_中国科学院金属研究所
通讯作者Liu, Jianchang
作者单位1.Univ Chinese Acad Sci, Beijing, 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.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110000, Liaoning, 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.

入库方式： OAI收割

来源：金属研究所

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