• DocumentCode
    577296
  • Title

    FRA-based transformer parameters at low frequencies

  • Author

    Pham, D.A.K. ; Pham, T.M.T. ; Safari, M.H. ; Ho, V.N.C. ; Borsi, H. ; Gockenbach, E.

  • Author_Institution
    Schering-Inst., Leibniz Univ. Hannover, Hannover, Germany
  • fYear
    2012
  • fDate
    17-20 Sept. 2012
  • Firstpage
    476
  • Lastpage
    479
  • Abstract
    The Frequency Response Analysis (FRA) is considered as an useful measure to detect deformation and changes of the active part of transformers. However, the corresponding interpretation is still subject of different investigation as there are many questions to be answered. One type of interpretation is based on waveform and statistical analyses, which is not fully efficient for all circumstances in reality. Recently there have been several researches mentioning another kind of interpretation that is based on equivalent electrical parameters of transformers extracted from measurements, but the problem is that there is a lack of “physical” verification for these parameters. Without such verification, they may be only valid in a certain context. To illustrate an example in which extracted parameters from FRA measurements are verified through other tests so that these parameters can be used for interpretation in all context, this paper introduces a method for extracting frequency responses of electrical parameters of a test transformer from FRA tests and their verifications through terminal/diagnostic tests performed by means of an universal testing device and a terminal testing set at low frequencies. Results shows that the extraction method can be applied to determine physical parameters of power transformers in such frequency range and these parameters are ready after verification as input sets for any machine-learning-based algorithm for the purpose of transformer failure diagnosis. The paper also belongs to a series of research tasks which deal with the issue of automatic and intelligent interpretation of FRA tests for power transformers.
  • Keywords
    deformation; failure analysis; frequency response; learning (artificial intelligence); power engineering computing; power transformer testing; statistical analysis; waveform analysis; FRA measurement-based power transformer parameter; deformation; equivalent electrical parameters; frequency response analysis; machine-learning-based algorithm; physical verification; statistical analyses; terminal testing set; terminal-diagnostic tests; transformer failure diagnosis; universal testing device; waveform; Frequency measurement; Impedance; Impedance measurement; Power transformers; Standards; Voltage measurement; Windings;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    High Voltage Engineering and Application (ICHVE), 2012 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-4747-1
  • Type

    conf

  • DOI
    10.1109/ICHVE.2012.6357083
  • Filename
    6357083