• DocumentCode
    2520033
  • Title

    Identification of partial discharges sources using a combination of linear prediction and neural networks

  • Author

    Medjeldi, T. ; Nemamcha, M. ; Gosse, J.P.

  • Author_Institution
    Centre Univ. de Guelma, Algeria
  • fYear
    1998
  • fDate
    22-25 Jun 1998
  • Firstpage
    165
  • Lastpage
    167
  • Abstract
    The ability to recognize forms by means of neural networks combined with a linear prediction analyze has been developed in this paper for partial discharges (PD) sources recognition. The PD sources are artificially created on a Teflon cell having two armatures isolated by polypropylene films. This experiments have been made at LEMD/CNRS Grenoble (France). This first work has been carried out by the use of a sample of seven specimens representing seven cases of presence of cavities on the cell insulation of study. The seven defects represented by respective signals of apparent charge were processed modeled by linear prediction and then passed to a back propagation neural networks
  • Keywords
    backpropagation; neural nets; partial discharges; prediction theory; Teflon cell; backpropagation; cavity; defect; form recognition; insulation; linear prediction; neural network; partial discharges source identification; polypropylene film; Artificial neural networks; Biological neural networks; Electrodes; Fault location; Insulation; Neural networks; Partial discharges; Signal processing; Time measurement; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Conduction and Breakdown in Solid Dielectrics, 1998. ICSD '98. Proceedings of the 1998 IEEE 6th International Conference on
  • Conference_Location
    Vasteras
  • Print_ISBN
    0-7803-4237-2
  • Type

    conf

  • DOI
    10.1109/ICSD.1998.709251
  • Filename
    709251