Wind and Structures

  • 제9권3호
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  • Pages.179-191
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  • 2006
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

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Errors in GEV analysis of wind epoch maxima from Weibull parents

  • Harris, R.I. (RWDI-Anemos Ltd.)
  • 투고 : 2005.03.09
  • 심사 : 2006.03.06
  • 발행 : 2006.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Parent wind data are often acknowledged to fit a Weibull probability distribution, implying that wind epoch maxima should fall into the domain of attraction of the Gumbel (Type I) extreme value distribution. However, observations of wind epoch maxima are not fitted well by this distribution and a Generalised Extreme Value (GEV) analysis leading to a Type III fit empirically appears to be better. Thus there is an apparent paradox. The reasons why advocates of the GEV approach seem to prefer it are briefly summarised. This paper gives a detailed analysis of the errors involved when the GEV is fitted to epoch maxima of Weibull origin. It is shown that the results in terms of the shape parameter are an artefact of these errors. The errors are unavoidable with the present sample sizes. If proper significance tests are applied, then the null hypothesis of a Type I fit, as predicted by theory, will almost always be retained. The GEV leads to an unacceptable ambiguity in defining design loads. For these reasons, it is concluded that the GEV approach does not seem to be a sensible option.

키워드

참고문헌

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피인용 문헌

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  2. Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean vol.87, pp.8, 2010, https://doi.org/10.1016/j.apenergy.2010.02.033
  3. Probability distributions for offshore wind speeds vol.52, pp.1, 2011, https://doi.org/10.1016/j.enconman.2010.06.015
  4. Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function vol.49, pp.5, 2008, https://doi.org/10.1016/j.enconman.2007.10.008
  5. Use of MinMaxEnt distributions defined on basis of MaxEnt method in wind power study vol.49, pp.4, 2008, https://doi.org/10.1016/j.enconman.2007.07.045
  6. XIMIS, a penultimate extreme value method suitable for all types of wind climate vol.97, pp.5-6, 2009, https://doi.org/10.1016/j.jweia.2009.06.011
  7. Technical note: Calibration of EVA methods for peak-over-threshold wind data using Bootstrapping vol.120, 2013, https://doi.org/10.1016/j.jweia.2013.07.005
  8. Three issues concerning the statistics of mean and extreme wind speeds vol.125, 2014, https://doi.org/10.1016/j.jweia.2013.12.009
  9. A joint probability distribution model of directional extreme wind speeds based on the t-Copula function vol.25, pp.3, 2006, https://doi.org/10.12989/was.2017.25.3.261
  10. Inverse Log-logistic distribution for Extreme Wind Speed modeling: Genesis, identification and Bayes estimation vol.6, pp.6, 2006, https://doi.org/10.3934/energy.2018.6.926