• DocumentCode
    1895043
  • Title

    Study on Line Selection of Grounded Fault in Non-effectively Earthed System Based on Wavelet Packet and Chaotic Neural Networks

  • Author

    Wang Nian-bin ; Wang Yan-Wen ; Gao Yan ; Fei, Long

  • Author_Institution
    China Univ. of Min. & Technol., Beijing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    10-11 Oct. 2009
  • Firstpage
    476
  • Lastpage
    480
  • Abstract
    According to the characteristic of wavelet packet transformation and chaotic neural networks, this paper proposed a new method of discriminating fault line for single-phase ground in distribution system based on the wavelet packet transformation and chaotic neural networks. The wavelet packet chaotic neural networks trained by the post fault zero sequence current and zero sequence voltage are used to discriminate the fault line. This method is suitable for any distribution system, not influenced by load problem of the traditional methods that select fault line incorrectly. The EMTP shows that the proposed method is accurate and reliable, and has great application potential.
  • Keywords
    EMTP; chaos; earthing; neural nets; power distribution faults; power distribution lines; wavelet transforms; EMTP; chaotic neural network; distribution system; fault line selection; fault zero sequence current; grounded fault line; noneffectively earthed system; wavelet packet transformation; zero sequence voltage; Chaos; Fault diagnosis; Frequency; Grounding; Neural networks; Power system reliability; Power system security; Power system transients; Voltage; Wavelet packets; chaotic neural networks; fault line selection; non-effectively earthed system; wavelet packet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
  • Conference_Location
    Changsha, Hunan
  • Print_ISBN
    978-0-7695-3804-4
  • Type

    conf

  • DOI
    10.1109/ICICTA.2009.122
  • Filename
    5287608