• DocumentCode
    2471541
  • Title

    Power transformer lifetime modeling

  • Author

    Dan Zhou ; Chengrong Li ; Zhongdong Wang

  • Author_Institution
    North China Electr. Power Univ., Beijing, China
  • fYear
    2012
  • fDate
    23-25 May 2012
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    As large proportions of power transformers are approaching or have exceeded their design life, concerns have aroused at their impact on the reliability of power networks, and forward replacement planning/budgeting is therefore required. In this paper, an appropriate transformer lifetime model is recognized as the key for the accurate replacement volume prediction. Since transformer failures are rare events in most of the electric utilities, industry-wide reliability data reflecting global perspective on transformer lifetime are recognized as good sources for formulating baseline models. A Bayesian Updating procedure is then proposed to incorporate the prior knowledge on the distribution of transformer lifetime (the baseline model) with available field failure data. Sequentially updating the model whenever new failure occurs allows the existing lifetime model to be improved in a progressive manner.
  • Keywords
    Bayes methods; life testing; power transformers; Bayesian updating; baseline model; design life; electric utilities; forward replacement planning/budgeting; power networks reliability; power transformer lifetime modeling; transformer failures; transformer lifetime distribution; Bayesian methods; Hazards; Indexes; Power transformers; Predictive models; Reliability; Standards; asset management; bayesian updating; lifetime model; power transformer; replacement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Prognostics and System Health Management (PHM), 2012 IEEE Conference on
  • Conference_Location
    Beijing
  • ISSN
    2166-563X
  • Print_ISBN
    978-1-4577-1909-7
  • Electronic_ISBN
    2166-563X
  • Type

    conf

  • DOI
    10.1109/PHM.2012.6228952
  • Filename
    6228952