• DocumentCode
    632288
  • Title

    Wind speed extreme quantiles estimation

  • Author

    Chiodo, Elio

  • Author_Institution
    Dept. of Electr. Eng. & Inf. Technol., Univ. of Naples Federico II, Naples, Italy
  • fYear
    2013
  • fDate
    11-13 June 2013
  • Firstpage
    760
  • Lastpage
    765
  • Abstract
    Wind speed (WS) probability distribution identification and estimation are the object of an increasing number of studies, especially related to the need of wind energy production evaluation. In this framework, the paper highlights the characterization of extreme WS quantiles, whose values and estimates are very sensitive to the assumed distributional form. This is a crucial issue not only for wind energy production assessment, but also in risk and reliability analysis. For the above purposes, the Lomax model is theoretically deduced and analysed: this model, indeed, well represents the typical “heavy tails” in WS probabilistic distributions arising from field data. A proper Bayes approach for the estimation of both the Lomax survivor function and of the above quantiles is analyzed. A large set of numerical simulations has been performed, and some typical subsets of them are shown to illustrate the efficiency of the estimates, showing excellent results.
  • Keywords
    power generation reliability; probability; wind power plants; Bayes approach; Lomax model; Lomax survivor function; WS probability distribution identification; numerical simulation; reliability analysis; risk analysis; wind energy production assessment; wind energy production evaluation; wind speed extreme quantile estimation; wind speed probability distribution identification; Analytical models; Maximum likelihood estimation; Numerical models; Reliability; Wind power generation; Wind speed; Bayes estimation; Gamma distribution; Lomax distribution; Wind Power;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Clean Electrical Power (ICCEP), 2013 International Conference on
  • Conference_Location
    Alghero
  • Print_ISBN
    978-1-4673-4429-6
  • Type

    conf

  • DOI
    10.1109/ICCEP.2013.6586944
  • Filename
    6586944