Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network | |
Zhang T(张彤)1![]() ![]() ![]() | |
Department | 工业控制网络与系统研究室 |
Source Publication | Electric Power Components and Systems
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ISSN | 1532-5008 |
2018 | |
Volume | 46Issue:9Pages:985-996 |
Indexed By | SCI ; EI |
EI Accession number | 20185206299723 |
WOS ID | WOS:000458114900001 |
Contribution Rank | 2 |
Keyword | Active distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU) |
Abstract | 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. |
Language | 英语 |
WOS Subject | Engineering, Electrical & Electronic |
WOS Keyword | PRINCIPAL COMPONENT ANALYSIS ; BASIS EXPANSIONS ; FUZZY-LOGIC ; CLASSIFICATION ; LINE ; ALGORITHM ; SCHEME ; MODEL |
WOS Research Area | Engineering |
Funding Project | IAPI Fundamental Research Funds[2013ZCX02-03] ; National Natural Science Foundation of China (NSFC)[61374137] ; National Natural Science Foundation of China (NSFC)[61773106] ; National Natural Science Foundation of China (NSFC)[61703086] ; National Key RD Program[2017YFB0902900] ; Fundamental Research Funds for the Central Universities[N160403003] ; IAPI Fundamental Research Funds[2013ZCX02-03] ; National Natural Science Foundation of China (NSFC)[61374137] ; National Natural Science Foundation of China (NSFC)[61773106] ; National Natural Science Foundation of China (NSFC)[61703086] ; National Key RD Program[2017YFB0902900] ; Fundamental Research Funds for the Central Universities[N160403003] |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/23943 |
Collection | 工业控制网络与系统研究室 |
Corresponding Author | Sun LX(孙兰香) |
Affiliation | 1.Institute of Automation, College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 2.Liaoning Province, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.Key Laboratory of Networked Control System, CAS, Shenyang, China 5.University of Chinese Academy of Sciences, Beijing, China 6.Liaoning Electric Power Compony Limited of State Grid, Shenyang, China 7.Yingkou Electric Power Supply Company, State Grid Liaoning Electric Power Supply Company Ltd, Liaoning, China |
Recommended Citation GB/T 7714 | Zhang T,Sun LX,Liu JC,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 | Zhang T.,Sun LX.,Liu JC.,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 | Zhang T,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. |
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Fault Diagnosis and (1322KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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