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Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis
Zhang T(张彤)1; Liu JC(刘建昌)1; Sun LX(孙兰香)2,3,4; Yu HB(于海斌)2,3,4; Zhang, Yingwei1
Department工业控制网络与系统研究室
Source PublicationJOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
ISSN0253-3839
2018
Volume41Issue:5Pages:375-386
Indexed BySCI ; EI
EI Accession number20183705802147
WOS IDWOS:000443901100002
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China ; National High Technology Research and Development Program of China ; IAPI Fundamental Research Funds
KeywordAnn Neural Network Phase Angle Active Distribution Network (Adn) Fault Diagnosis
Abstract

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.

Language英语
WOS SubjectEngineering, Multidisciplinary
WOS KeywordDistribution-systems ; Transmission-lines ; Identification ; Algorithm ; Svm
WOS Research AreaEngineering
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]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22763
Collection工业控制网络与系统研究室
Corresponding AuthorLiu JC(刘建昌)
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang, China
2.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China
3.Key Laboratory of Networked Control System, CAS, Shenyang, China
4.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Zhang T,Liu JC,Sun LX,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 T,Liu JC,Sun LX,Yu HB,&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 T,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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