• DocumentCode
    3019760
  • Title

    Study on fault diagnosis of power transformer based on RSNN

  • Author

    Lv Hong-li

  • Author_Institution
    Dept. of Inf. Eng., Tangshan Coll., Tangshan, China
  • fYear
    2013
  • fDate
    20-22 Dec. 2013
  • Firstpage
    344
  • Lastpage
    347
  • Abstract
    This paper studies the power transformer fault quality diagnosis using the technology of intelligent diagnosis. It is rough set theory as the pre-unit of neural network. A large number of original data is made reduction by means of rough set algorithm and become the train data of BP neural network. Through simulation with practical data it is proved that the method of RSNN can make the training time shorter and the diagnostic accuracy is higher compared with the traditional neural network method.
  • Keywords
    backpropagation; fault diagnosis; neural nets; power engineering computing; power transformers; rough set theory; BP neural network; RSNN; fault diagnosis; intelligent diagnosis; power transformer fault quality diagnosis; rough set theory; Accuracy; Discharges (electric); Fault diagnosis; Neural networks; Power transformers; Set theory; Training; fault diagnosis; neural network; power transformer; rough set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
  • Conference_Location
    Shengyang
  • Print_ISBN
    978-1-4799-2564-3
  • Type

    conf

  • DOI
    10.1109/MEC.2013.6885095
  • Filename
    6885095