• 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
模糊支持向量机在变压器故障诊断中的应用
Application of Fuzzy Support Vector Machine in Transformer Fault Diagnosis
Received:April 13, 2014  Revised:April 13, 2014
DOI:
中文关键词: 模糊支持向量机  故障诊断  模糊C均值算法  变压器
英文关键词: fuzzy support vector machines  fault diagnosis  fuzzy C-means clustering algorithm  transformer
基金项目:
Author NameAffiliationE-mail
SHI Li-ping School of Information and Engineering,China University of Mining and Technology 727953405@qq.com 
YU Peng-xi* School of Information and Engineering,China University of Mining and Technology cyyupxi@163.com 
LUO Peng School of Information and Engineering,China University of Mining and Technology  
XU Tian-ran School of Information and Engineering,China University of Mining and Technology  
LIU Peng School of Information and Engineering,China University of Mining and Technology  
LI Jia-jia School of Information and Engineering,China University of Mining and Technology  
Hits: 2051
Download times: 797
中文摘要:
      为了解决在变压器故障诊断时复杂多样难以辨识的问题,有效提高诊断准确率,本文提出了采用模糊支持向量机构建变压器故障诊断模型的方法。该方法首先把变压器的故障分为两大类,利用交叉验证和网格搜索相结合的方法对模糊支持向量机进行参数寻优,用模糊C均值算法求取模糊支持向量机的隶属度。实验表明,该方法比改良IEC比值法和传统支持向量机法具有更高的准确率,更适用于变压器故障诊断。
英文摘要:
      In order to solve the problem of correctly identifying fault classes in transformer fault diagnosis and improve the accuracy of the faule diagnosis, a novel fault diagnosis method of the transformer model based on fuzzy support vector machine(FSVM) in this paper. In this method, transformer faults are firstly divided into two types , and the gird search method based on cross-validation is chosen to determine the optimized parameters of the FSVM model .The membership value of the FSVM is obtained by fuzzy C-means clustering alaorithm. The experiment shows that the proposed method is more effective and accurate than the method of IEC and normal SVM, and this method is proper in fault diagnosis of transformer.
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