Simulation of Offshore Wind Turbine Response for Long-term Ultimate Loads

Agarwal, P., Manuel, L.


When there is interest in estimating long-term extreme loads for an offshore wind turbine using simulation, statistical extrapolation is the method of choice.  While the method itself is rather well-established, simulation effort can be intractable if uncertainty in predicted extreme loads and efficiency in the selected extrapolation procedure are not specifically addressed.  Our aim in this study is to address these questions in predicting blade and tower extreme loads based on stochastic response simulations of a 5 MW offshore turbine.  We illustrate the use of the peak-over-threshold method to predict long-term extreme loads.  To derive these long-term loads, we employ an efficient inverse reliability approach which is shown to predict reasonably accurate long-term loads when compared to the more expensive direct integration of conditional load distributions for different environmental (wind and wave) conditions.  Fundamental to the inverse reliability approach is the issue of whether turbine response variability conditional on environmental conditions is modeled in detail or whether only gross conditional statistics of this conditional response are included.  We derive long-term loads for both these cases, and demonstrate that careful inclusion of response variability not only greatly influences such long-term load predictions but it also identifies different environmental conditions that bring about these long-term loads compared to when response variability is only approximately modeled.  As we shall see, for this turbine, a major source of response variability for both the blade and tower arises from blade pitch control actions due to which a large number of simulations is required to obtain stable distribution tails for the turbine loads studied.


Agarwal, P. and Manuel, L., “Simulation of Offshore Wind Turbine Response for Long-term Ultimate Loads,” Engineering Structures, Vol. 31, Issue 10, pp. 2236-2246, Oct 2009.