Efficient ultimate load estimation for offshore wind turbines using interpolating surrogate models

L M M Van Den Bos,L Blonk,W A A M Bierbooms,G J W Van Bussel,B Sanderse

doi:10.1088/1742-6596/1037/6/062017

Abstract

During the design phase of an offshore wind turbine, it is required to assess the impact of loads on the turbine life time. Due to the varying environmental conditions, the effect of various uncertain parameters has to be studied to provide meaningful conclusions. Incorporating such uncertain parameters in this regard is often done by applying binning, where the probability density function under consideration is binned and in each bin random simulations are run to estimate the loads. A different methodology for quantifying uncertainties proposed in this work is polynomial interpolation, a more efficient technique that allows to more accurately predict the loads on the turbine for specific load cases. This efficiency is demonstrated by applying the technique to a power production test problem and to IEC Design Load Case 1.1, where the ultimate loads are determined using BLADED. The results show that the interpolating polynomial is capable of representing the load model. Our proposed surrogate modeling approach therefore has the potential to significantly speed up the design and analysis of offshore wind turbines by reducing the time required for load case assessment.

Highlights

It is required to assess various load cases during the design phase of a wind turbine
Design Load Case 1.1 applied to the NREL 5MW Baseline Wind turbine Design Load Case (DLC) 1.1 encompasses assessing the effect of normal wind and sea conditions during normal power production on the ultimate loads of the turbine
The full probability density function (PDF) can be written as Annual average wind speed, Vave Turbulence intensity, Iref Mean wind speed at hub height, Vhub Longitudinal turbulence intensity, I

Summary

Introduction

It is required to assess various load cases during the design phase of a wind turbine. Due to the uncertain environmental conditions, the wind and sea conditions have to be modeled stochastically according to a prescribed distribution. To determine the influence of uncertain parameters, it is customary to first divide the ranges of the distributions under consideration in bins, subsequently to randomly draw samples in each bin (the so-called seeds), and to accumulate the results. This is suggested in the aforementioned standard [1] and commonly applied in literature In case of uncertain (mean) wind speed, often the bins are chosen independently from the distribution under consideration and solely depend on the cut-in and cut-out speed of the wind turbine

Objectives

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Conclusion