On the propagation of uncertainty in inflow turbulence to wind turbine loads

When stochastic simulation of inflow turbulence random fields is employed in the analysis or design of wind turbines in normal operating states, it is common to use well-established standard spectral models represented in terms of parameters that are usually treated as fixed or deterministic values. Studies have suggested, though, that many of these spectral parameters can exhibit some degree of variability. It is not unreasonable to expect, then, that derived flow fields based on simulation with such spectral models can be in turn highly variable for different realizations. Turbine load and performance variability would also be expected to result if response simulations are carried out with these variable flow fields. The aim here is to assess the extent of variability in derived inflow turbulence fields that arises from the noted variability in spectral model parameters. Simulation of these parameters as random variables forms the basis of this study. A commercial-sized 1.5 MW concept wind turbine is considered in the numerical studies. Variability in turbulence power spectra at field points on the rotor plane and in turbulence coherence functions for separations on the order of a rotor diameter and smaller is studied. Using time domain simulations, variability in various wind turbine response measures is also studied where the focus is on statistics such as response root-mean-square and 10-min extreme estimates. It is seen that while variability in inflow turbulence spectra can be great, the variability in turbine loads is generally considerably lower. One exception is for turbine yaw loads whose larger variability arises due to sensitivity to a coherence decay parameter that is itself highly variable. Finally, because reduced-order representations of turbulence random fields using empirical orthogonal decomposition techniques allow useful physical insights into spatial patterns of flow, variability in the energy distribution and the shapes of such empirical eigenmodes is studied and a simplified model is proposed that retains key variability sources in a limited number of modes and that accurately preserves overall inflow turbulence field uncertainty. (c) 2008 Elsevier Ltd. All rights reserved.