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Extreme Operational Response of OWTS – Application of FORM

Extreme Operational Response of OWTS – Application of FORM. Niels Jacob Tarp-Johansen Wind Energy Dept. Risø National Laboratory Presented at OWEMES 2006, April 20-22, 2006 (Rome). Outline. Background Methods Parametric FORM IFORM Example Environmental conditions + Turbine response

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Extreme Operational Response of OWTS – Application of FORM

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  1. Extreme Operational Response of OWTS – Application of FORM Niels Jacob Tarp-Johansen Wind Energy Dept. Risø National Laboratory Presented at OWEMES 2006, April 20-22, 2006 (Rome)

  2. Outline • Background • Methods • Parametric • FORM • IFORM • Example • Environmental conditions + Turbine response • Results • Concluding remarks

  3. Background 1/2 • Why? • Operational loads compare with storm loads • Duration in operation • Large loads during operation Operation Storm • Dynamic response (power build-up) • Quasi-static response (large gusts) • Is there an upper limit to max response?

  4. Background 2/2 • Problem • 50 yr max response is desired • Monte Carlo simulation would require N times 50 yr • 50∙365∙24∙6 ≈ 2.6∙106 10-min simulations in 50 yr • NOT practicable • Solution: extrapolation • Fit probability distributions to relatively few simulations • Determine expected 50 yr extreme by extrapolation • OR: compute extreme deterministically if there is an upper limit

  5. Methods 1/3 • Parametric • Determine long-term 10-min max distribution • Solve for the 1-1/N ’th fractile • Requires simulations for each combination of the environmental parameters • Mixes distribution of environmental parameters and response variability

  6. Methods 2/3 • FORM • Alternative computational scheme • Approximate • As a bi-product it delivers the parameter combination of environment and response that most likely generates the extreme response • Requires quite some simulations too • Aim: to provide a generic FORM analysis that proposes a small set of environmental parameters to consider in extrapolation and focus simulations on these parameters • pull distribution of environmental parameters and response variability from each other

  7. Methods 3/3 • IFORM (Inverse FORM) • Provides a set of 50-yr return period environmental conditions – the environmental contour • Determine the extreme response as the extreme among the response along the contour • Requires fewer simulations • But for more variables larger contour to search • Neglects response variability • Assumes continuity of response with environmental parameters

  8. Example 1/5 • Environment – no wave load yet • IEC 61400-1: IA, IB and IIA

  9. Example 2/5 • Turbine response – no wave load • Pitch regulated I = 10%

  10. Example 3/5 IA + Gumbel Extrapolation of response variability plays a major role

  11. Example 4/5 IA , IB , IIA + Gumbel

  12. Example 5/5 IA , IB , IIA + Log-Normal

  13. Concluding remarks • In extrapolation of response variability plays a major role: • IFORM has its limitations • Parametric method is demanding • Generic FORM may be acceptable when considering the statistical uncertainty and the benefit from the fact that typically a narrow range of wind speeds is essential

  14. Concluding remarks • Other uncertainty sources from physical modelling may be just as relevant: • turbulence modelling • other climate conditions: passage of weather fronts • aerodynamic model • control system reliability • Future investigations • Other turbine control systems • Include wave loads at different water depths, i.e. varying dynamic sensitivity to waves • Influence of wind-wave misalignment and wakes

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