• DocumentCode
    2344196
  • Title

    Forecasting of polluted insulator flashover based on multivariate nonlinear time series analysis

  • Author

    Jianyuan, Xu ; Yun, Teng ; Xin, Lin

  • Author_Institution
    Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang
  • fYear
    2009
  • fDate
    25-27 May 2009
  • Firstpage
    3694
  • Lastpage
    3697
  • Abstract
    To solve the problem of the flashover forecasting of contaminated or polluted insulator, a flashover forecasting model of contaminated insulators based on multivariate nonlinear time series analysis is proposed in the paper. The equivalent salt deposit density (ESDD) is the key of flashover on polluted insulator. The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper. The forecasting model consists of two artificial neural networks that reflect relationship of environment, ESDD and flashover probability. The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover. A series of artificial pollution tests show that the results of the forecasting model is acceptable.
  • Keywords
    flashover; insulator contamination; neural nets; power engineering computing; probability; time series; ESDD; artificial neural network; contaminated insulator; equivalent salt deposit density; multivariate nonlinear time series analysis; polluted insulator flashover forecasting; probability; Artificial neural networks; Chaos; Dielectrics and electrical insulation; Flashover; Load forecasting; Pollution measurement; Power system reliability; Predictive models; Technology forecasting; Time series analysis; Polluted insulator flashover; artificial neural networks; multivariate nonlinear time series;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4244-2799-4
  • Electronic_ISBN
    978-1-4244-2800-7
  • Type

    conf

  • DOI
    10.1109/ICIEA.2009.5138892
  • Filename
    5138892