Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network | |
Zhang T(张彤)4; Sun LX(孙兰香)2,5,6; Liu JC(刘建昌)4; Yu HB(于海斌)2,5,6; Zhou, Xiaoming3; Gao, Lin1; Zhang, Yingwei4 | |
刊名 | Electric Power Components and Systems |
2018 | |
卷号 | 46期号:9页码:985-996 |
关键词 | Active distribution network (ADN) fault location analysis high resistance fault phase measurement unit (PMU) |
ISSN号 | 1532-5008 |
产权排序 | 2 |
英文摘要 | 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. |
资助项目 | 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] |
WOS关键词 | PRINCIPAL COMPONENT ANALYSIS ; BASIS EXPANSIONS ; FUZZY-LOGIC ; CLASSIFICATION ; LINE ; ALGORITHM ; SCHEME ; MODEL |
WOS研究方向 | Engineering |
语种 | 英语 |
WOS记录号 | WOS:000458114900001 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/23943] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Sun LX(孙兰香) |
作者单位 | 1.Yingkou Electric Power Supply Company, State Grid Liaoning Electric Power Supply Company Ltd, Liaoning, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Liaoning Electric Power Compony Limited of State Grid, Shenyang, China 4.Institute of Automation, College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning Province, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 6.Key Laboratory of Networked Control System, CAS, Shenyang, China |
推荐引用方式 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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