Extreme value predictions for application in wind turbine design are often based on asymptotic results. This is typically done by assuming the epochal extremes in a 10 minute interval are distributed according to some asymptotic extreme value distribution with unknown parameters to be estimated based on simulated low order statistical moments, or it is assumed that the exceedance probability above high thresholds follows a Pareto distribution with parameters to be estimated.
The results obtained by an extrapolation of the extreme values to the stipulated design period of the wind turbine depend strongly on the relevance of these adopted extreme value distributions. The problem is that this relevance cannot be decided from the data obtained by the indicated so-called crude Monte Carlo method. With failure probabilities of the magnitude 1e-9 during a 10 min. sampling interval the tails of the distributions are never encountered during normal operations. To circumvent this problem the application of variance reduction Monte Carlo methods i.e. importance sampling method might be used, which suffer from strict requirement on the so called simulation density for a high dimensional parameter vector.
Finally splitting methods are lately reported to be more promising for efficient estimation of extreme responses of wind turbines. Firstly they support certain level of accuracy in results and secondly they are more efficient in computation since they have a less strict constraint on margins of the safe domain. This approach will be followed in the project.
|Period||01-08-08 → 31-07-11|
|Research programme||<ingen navn>|
Estimation of Extreme Responses and Failure Probability of Wind Turbines under Normal Operation by Controlled Monte Carlo Simulation
Publication: Research › Ph.d. thesis