Further study of probabilistic methods for predicting extreme wind turbine loading was performed on two large-scale wind turbine models with stall and pitch regulation. Long-term exceedance probability distributions were calculated using maxima extracted from time series simulations of in-plane and out-of-plane blade loads. It was discovered that using a threshold on the selection of maxima increased the accuracy of the fitted distribution in following the trends of the largest extreme values for a given wind condition. The optimal threshold value for in-plane and out-of-plane blade loads was found to be the mean value plus 1.4 times the standard deviation of the original time series for the quantity of interest. When fitting a distribution to a given data set, the higher-order moments were found to have the greatest amount of uncertainty and also the largest influence on the extrapolated long-term load’s. This uncertainty was reduced by using large data sets, smoothing of the moments between wind conditions and parametrically modeling moments of the distribution. A deterministic turbulence model using the 90th percentile level of the conditional turbulence distribution given mean wind speed was used to greatly simplify the calculation of the long-term probability distribution. Predicted extreme loads using this simplified distribution were equal to or more conservative than the loads predicted by the full integration method.
Probabilistic Methods for Predicting Wind Turbine Design Loads
Moriarty, PJ, Holley, WE, & Butterfield, S. "Probabilistic Methods for Predicting Wind Turbine Design Loads." Proceedings of the ASME 2003 Wind Energy Symposium. ASME 2003 Wind Energy Symposium. Reno, Nevada, USA. January 6–9, 2003. pp. 235-243. ASME. https://doi.org/10.1115/WIND2003-864
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