• DocumentCode
    2798567
  • Title

    Wavelet Neural Network based fault detection method in power system

  • Author

    Xiaohua, Yang ; Yadong, Zhang ; Faqi, Zhao ; Zhongmei, Xi

  • Author_Institution
    Shandong Province Key Lab. of horticultural Machineries & Equipments, Shandong Agric. Univ., Tai´´an, China
  • fYear
    2011
  • fDate
    15-17 July 2011
  • Firstpage
    1864
  • Lastpage
    1867
  • Abstract
    Wavelet Neural Network combined the advantages of wavelet transform and neural network, It is a knowledge-based fault diagnosis method It doesn´t need accurate mathematical model, both have good time-frequency localization properties and better self-learning ability and fault tolerance. This article describes the natural network in power system fault detection, the simulation results show that, compared with the traditional artificial neural network, the wavelet neural network has the characteristics of fast convergence. So wavelet neural network can be applied to power system fault detection.
  • Keywords
    fault diagnosis; fault tolerance; knowledge based systems; neural nets; power system analysis computing; wavelet transforms; fault tolerance; knowledge-based fault diagnosis method; power system fault detection; self-learning ability; time-frequency localization; wavelet neural network; wavelet transform; Biological neural networks; Circuit faults; Neurons; Power systems; Wavelet analysis; Wavelet domain; Wavelet transforms; Wavelet neural network; electric power system; failure testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
  • Conference_Location
    Hohhot
  • Print_ISBN
    978-1-4244-9436-1
  • Type

    conf

  • DOI
    10.1109/MACE.2011.5987327
  • Filename
    5987327