• DocumentCode
    1762228
  • Title

    Bayes Monte-Carlo Assessment Method of Protection Systems Reliability Based on Small Failure Sample Data

  • Author

    Zhihui Dai ; Zengping Wang ; Yanjun Jiao

  • Author_Institution
    North China Electr. Power Univ., Baoding, China
  • Volume
    29
  • Issue
    4
  • fYear
    2014
  • fDate
    Aug. 2014
  • Firstpage
    1841
  • Lastpage
    1848
  • Abstract
    Reliability assessment methods that need large failure sample sets would face a bottleneck while the “high reliability” of the protection system is taken into account and would, therefore, directly affect the credibility of the assessment results. To overcome this inadequacy, a protection system reliability assessment method based on small failure samples was proposed. First, after analyzing the operating characteristics of protection devices and its small failure sample data, the Weibull distribution was selected as the failure distribution through the goodness-of-fit test. Second, the parameters of the Weibull distribution were estimated by regression analysis, and Monte-Carlo simulation was used to determine the prior distribution of the Bayes estimation. The posterior probability and reliability indices were then obtained by using the Bayes theory. The case study demonstrates the effectiveness of the method.
  • Keywords
    Bayes methods; Monte Carlo methods; Weibull distribution; failure analysis; power system protection; power system reliability; regression analysis; Bayes Monte-Carlo assessment method; Bayes estimation; Bayes theory; Monte-Carlo simulation; Weibull distribution; failure distribution; failure sample data; protection devices; protection system reliability assessment method; protection systems reliability; regression analysis; Estimation; Monte Carlo methods; Power system reliability; Reliability theory; Weibull distribution; Protective relaying; reliability assessment; small failure sample;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/TPWRD.2014.2316915
  • Filename
    6807822