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The Good, the Bad and the Ugly Extreme Wind

The Good, the Bad and the Ugly Extreme Wind. Wiebke Langreder 1 Jørgen Højstrup 1 Lasse Svenningsen 2 1 Suzlon Energy A/S, Denmark 2 EMD A/S, Denmark. Contents (Part 3). The task: Mission Impossible? What we have done so far What is new Our results and recommendations Outlook.

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The Good, the Bad and the Ugly Extreme Wind

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  1. The Good, the Bad and the Ugly Extreme Wind Wiebke Langreder1 Jørgen Højstrup1 Lasse Svenningsen2 1 Suzlon Energy A/S, Denmark 2 EMD A/S, Denmark

  2. Contents (Part 3) • The task: Mission Impossible? • What we have done so far • What is new • Our results and recommendations • Outlook

  3. Terminology • Extreme Wind = Maximum 10-minute average wind speed with recurrence period 50 years • In IEC language: Vref

  4. Inappropriate Positive Thinking? Denmark 1999 Japan Spain 2009

  5. Predict maximum 10-minute average wind speed in 50 years. Normal situation: 1-5 years of data Extreme winds are not related to mean wind speed. The task: Mission Impossible?

  6. Objective: Choose method to Minimize uncertainty Minimize bias The task: Mission Impossible?

  7. Contents • The task: Mission Impossible? • What we have done so far • What is new • Our results and recommendations • Outlook

  8. Long-time series are split in shorter sub-sets, each method is applied to each sub-set. Sub-set 1 → Vref Sub-set 2 → Vref Sub-set 3 → Vref Sub-set 4 → Vref Sub-set 5 → Vref Establish Method LT

  9. Assumption The “true” Vref is determined: using full data set extracting Annual Maxima (Periodical Maxima) Gumbel distribution ”True” Reference Value

  10. Normalisation with this ”true” value Sub-set 1 → Vref Sub-set 2 → Vref Sub-set 3 → Vref Sub-set 4 → Vref Sub-set 5 → Vref Method PM: LT → ”True” Vref N subsets → N results per method → Standard deviation → Bias

  11. Previous Methods • EWTS European Wind Turbine Standard Vref depending on k factor • 360 degree • sector with highest mean v • PM Periodical Maximum • POT Peak-over-threshold Gumbel

  12. Contents • The task: Mission Impossible? • What we have done so far • What is new • Our results and recommendations • Outlook

  13. New development • Parameter describing Gumbel distribution are determined graphically

  14. New development Possible reasons for non-linearity: • Wrong way to extract extreme events? • Wrong way to plot/fit? • No convergence towards Gumbel?

  15. Better extraction/plotting IMIS - Improved method of independent storms (Cook/Harris) Different two-stage process to extract Different way to fit regression

  16. Improved convergence • Samples extracted from Weibull parent not necessarily exponential • Slow convergence towards Gumbel (exponential) • Pre-conditioning • Substitution of V with Vc High end of Vc → exponential Gumbel → exponential Tatata: faster convergence

  17. Pre-conditioning Two methods: V2 (dynamic pressure) Vk (Weibull shape parameter (Cook/Harris))

  18. Additional New Development • Effect of measurement period: Length of sub-sets: 1, 2, 3 and 5 years

  19. Contents • The task: Mission Impossible? • What we have done so far • What is new • Our results and recommendations • Outlook

  20. 15 sites (Europe, US, Asia, Roaring 40th) 158 1 year periods 77 2 year periods 49 3 year periods 22 5 year periods Statistical relevance

  21. Results Different Methods Pre-conditioning Period

  22. Result - EWTS • EWTS 360degr lowest results • EWTS max similar numbers as PM-POT-IMIS • based on distribution → less sensitive to actual period  • very difficult to identify ”correct” sector

  23. Recommendation (1/3) • Use POT 2 (= dynamic pressure) • lowest standard deviation and lowest standard error of the mean for 1 year periods Disadvantage: • Result very sensitive to highest measured wind speed in measurement period

  24. Recommendation (2/3) • Use EWTS max (sector with the highest average wind speeds) Advantage: • Independent of period Disadvantage: • Difficult to identify sector

  25. Recommendation (3/3) Combine the two methods • Engineering approach, taking the average of EWTS (max) and POT 2

  26. Outlook • Check sensitivity to outlier • If Vref depends on highest measured wind speed: Better results for a X year data set by using POT for each year seperately and then average? • Try correlation with NCEP/NCAR to find out about level of highest measured wind speed in a sample

  27. Acknowledgement Thanks to • www.winddata.com • www.undeerc.org/wind • www.bom.gov.au/inside/cgbaps

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