IMR OpenIR
Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis
Zhang, Tong1; Liu, Jianchang1; Sun, Lanxiang2,3,4; Yu, Haibin2,3,4; Zhang, Yingwei1
Corresponding AuthorLiu, Jianchang(liujianchang@ise.neu.edu.cn)
2018
Source PublicationJOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
ISSN0253-3839
Volume41Issue:5Pages:375-386
AbstractThe 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.
KeywordANN neural network phase angle active distribution network (ADN) fault diagnosis
Funding OrganizationNational Natural Science Foundation of China ; National High Technology Research and Development Program of China ; IAPI Fundamental Research Funds
DOI10.1080/02533839.2018.1490204
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaEngineering
WOS SubjectEngineering, Multidisciplinary
WOS IDWOS:000443901100002
PublisherCHINESE INST ENGINEERS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/129405
Collection中国科学院金属研究所
Corresponding AuthorLiu, Jianchang
Affiliation1.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
Recommended Citation
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.
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