• DocumentCode
    637437
  • Title

    Characteristic parameters prediction of power transformer based on discrete gray model

  • Author

    Kai Wang

  • Author_Institution
    Substation Oper. Dept. 2, Henan Zhengzhou Power Supply Co., Zhengzhou, China
  • fYear
    2012
  • fDate
    18-20 Sept. 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Characteristic parameters prediction of power transformer can be used to predict the condition or fault of power transformer. On the basis of the acknowledgement of the applicability of gray model for power transformer characteristic parameters, an approach to predict of characteristic parameters based on the discrete gray model(DGM) is introduced into the characteristic parameters prediction of power transformer, as a solution of avoiding the possible instability of the traditional gray model prediction. And the method of the firsthand data preprocessing, as data of the characteristic parameters of power transformer have the characteristics of strong random and non-equidistance, is proposed to make it suitable for the characteristic parameters data prediction and to improve the prediction accuracy, so that all the obstacles from the theoretical analysis to engineering application of the prediction of power transformer by predicting the condition parameters data with DGM are removed, and the accurate prediction of power transformer condition or transformer faults is achieved.
  • Keywords
    power system faults; power transformers; characteristic parameters prediction; data prediction; data preprocessing; discrete gray model; power transformer fault; transformer faults; Accuracy; Capacitance; Data models; Mathematical model; Oil insulation; Power transformers; Predictive models; characteristic parame ters prediction; condition; discrete gray model; power transformer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering and Automation Conference (PEAM), 2012 IEEE
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4577-1599-0
  • Type

    conf

  • DOI
    10.1109/PEAM.2012.6612551
  • Filename
    6612551