• HOME
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • Chinese
Site search        
文章摘要
基于5G的配电网智能故障诊断方法
Intelligent fault diagnosis method of distribution network based on 5G
Received:January 18, 2021  Revised:February 17, 2021
DOI:10.19753/j.issn1001-1390.2024.04.003
中文关键词: 5G  GRNN  正、零序电流检测法  配电网  故障诊断
英文关键词: 5G, GRNN, positive  and zero  sequence current  detection method, distribution  network, fault  diagnosis
基金项目:国家自然科学基金(61563004),国家自然科学基金(61761007),广西研究生创新管理项目(YCSW2020061)
Author NameAffiliationE-mail
YAN Ming School of Computer and Electronic Information,Guangxi University 752337535@qq.com 
GUO Wenhao School of Computer and Electronic Information,Guangxi University 953025349@qq.com 
HU Yongle Runjian Co,Ltd huyongle@rjtx.net 
QIN Tuanfa* School of Computer and Electronic Information,Guangxi University tfqin@gxu.edu.cn 
Hits: 617
Download times: 222
中文摘要:
      配电网是电网中发生短路故障最多且智能化程度较低的地方。目前主要使用基于零序电流比幅法来进行接地故障的故障诊断,但存在非接地故障识别率低和无法快速识别等问题。采用5G通信技术,提出三序复合电流检测法并结合广义回归神经网络(GRNN,generalized regression neural network)来实现配电网故障诊断的实时传输与快速决策。测试结果表明可提升非接地故障的识别率达20%以上,解决了以往通信不可靠和故障诊断智能化不高等问题。
英文摘要:
      Distribution network is the place with the most short circuit faults and low degree of intelligence. At present, the zero-sequence current amplitude comparison method is mainly used for fault diagnosis of grounding fault, but there are some problems such as low recognition rate of non-grounding fault and unable to identify quickly. 5G communication technology is adopted, three-sequence composite current detection method is proposed, and generalized regression neural network (GRNN) is combined to realize real-time transmission and rapid decision-making of fault diagnosis in distribution network. The test results show that the recognition rate of non-grounding fault can be improved by more than 20%, which solves the problems of unreliable communication and low intelligent fault diagnosis.
View Full Text   View/Add Comment  Download reader
Close
  • Home
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • 中文页面
Address: No.2000, Chuangxin Road, Songbei District, Harbin, China    Zip code: 150028
E-mail: dcyb@vip.163.com    Telephone: 0451-86611021
© 2012 Electrical Measurement & Instrumentation
黑ICP备11006624号-1
Support:Beijing Qinyun Technology Development Co., Ltd