• DocumentCode
    1631280
  • Title

    Intelligent Data Mining Approach for Fault Diagnosis

  • Author

    Huang, Yann-Chang ; Sun, Huo-Ching ; Lin, Yu-Hsun

  • Author_Institution
    Dept. of Electr. Eng., Cheng Shiu Univ., Kaohsiung
  • Volume
    1
  • fYear
    2008
  • Firstpage
    303
  • Lastpage
    306
  • Abstract
    This paper presents wavelet analysis and statistical techniques for assessing the insulation condition of power cables. A specific fault is made and placed on the terminal joint of a 25 kV power cable, and the deterioration phenomena is accelerated by the overvoltage method. The deterioration phenomena of the internal insulation material are explained by wavelet analysis and statistical techniques using partial discharge (PD) current pulse waveforms. The PD value reaches its maximum level, and average discharge level rises, before insulation breakdown. However, the discharge numbers and the equivalent time-length of partial discharge current pulse waveforms fall, causing a current pulse with a large amplitude, and a short time period in the final stage of PD. The proposed method is demonstrated to be effective and feasible.
  • Keywords
    data mining; fault diagnosis; power cable insulation; power engineering computing; statistical analysis; wavelet transforms; data mining; fault diagnosis; insulation; partial discharge current pulse waveforms; power cables; statistical techniques; wavelet analysis; Circuit faults; Data mining; Fault diagnosis; Insulation; Partial discharge measurement; Partial discharges; Power cables; Signal processing; Underground power cables; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
  • Conference_Location
    Kaohsiung
  • Print_ISBN
    978-0-7695-3382-7
  • Type

    conf

  • DOI
    10.1109/ISDA.2008.201
  • Filename
    4696221