• DocumentCode
    13755
  • Title

    Icing load accretion prognosis for power transmission line with modified hidden semi-Markov model

  • Author

    Xin Wu ; Lin Li ; Xiaoming Rui

  • Author_Institution
    Sch. of Energy, Power & Mech. Eng., North China Electr. Power Univ., Beijing, China
  • Volume
    8
  • Issue
    3
  • fYear
    2014
  • fDate
    Mar-14
  • Firstpage
    480
  • Lastpage
    485
  • Abstract
    Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the icing load accretion on an interval of the power transmission lines with modified hidden semi-Markov model (HSMM). Based on the predicted RDT of the cables during ice accretion, the appropriate preventative measures can be scheduled in advance by electric power companies. The estimation model with the learning algorithm of support vector machine for icing load accretion is built through historical icing load accretion data and meteorological conditions first. Then, the estimated icing load accretion sequence can be obtained through the estimation model by forecasting the meteorological conditions. The modified HSMM method can eliminate the possible underflow issue during computation, and be used to build the RDT prognosis model. With the estimated icing load accretion sequence and RDT prognosis model, the authors can predict RDT of the icing load accretion on an interval of the power transmission lines. The developed prognosis algorithm is verified through collected meteorological conditions and icing load accretion data on the Dazheng 73# power transmission line in Zhaotong area, Yunnan Province, South China.
  • Keywords
    freezing; hidden Markov models; power cables; power engineering computing; power transmission lines; support vector machines; RDT 30 prognosis model; South China; Yunnan Province; Zhaotong area; cable failure; electric power companies; estimation model; historical icing load accretion data; icing load accretion prognosis; learning algorithm; meteorological conditions; modified hidden semiMarkov model; power transmission line; remaining-dangerous time; support vector machine;
  • fLanguage
    English
  • Journal_Title
    Generation, Transmission & Distribution, IET
  • Publisher
    iet
  • ISSN
    1751-8687
  • Type

    jour

  • DOI
    10.1049/iet-gtd.2013.0063
  • Filename
    6750592