• DocumentCode
    3392991
  • Title

    Transformer Fault Diagnosis Based on Rough Sets and Support Vector Machine

  • Author

    Li Zhi-bin ; Xie Zhi-hui

  • Author_Institution
    Coll. of Power & Autom., Shanghai Univ. of Electr. Power, Shanghai, China
  • fYear
    2012
  • fDate
    27-29 March 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    For the problem of little sample size and incomplete sample information which leads to the fact that fault diagnosis results are not ideal in the transformer fault diagnosis process, we combine the simplifying of rough sets with support vector machine classification .Then, we build the model of transformer fault diagnosis which is based on the rough sets and support vector machine .Proved by the simulation of true sample, this model can diagnosis the transformer fault effectively and has very high accuracy rate.
  • Keywords
    fault diagnosis; pattern classification; power engineering computing; power transformers; rough set theory; support vector machines; rough set theory; support vector machine classification; transformer fault diagnosis process; Accuracy; Fault diagnosis; Kernel; Power transformers; Support vector machines; Testing; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
  • Conference_Location
    Shanghai
  • ISSN
    2157-4839
  • Print_ISBN
    978-1-4577-0545-8
  • Type

    conf

  • DOI
    10.1109/APPEEC.2012.6307355
  • Filename
    6307355