Abstract
Recent studies have shown the advantage of replacing aeroelastic simulations with regression models based on Artificial Neural Networks (ANNs), which can be used as surrogate models for fast and efficient wind turbine load assessments. Once trained on a high-fidelity load simulation database covering a broad range of conditions, the surrogate model can be applied to predict loads for any site with wind climate falling within the range covered by the database. The aim of this study is to quantify the uncertainty propagation through such an ANN and to analyse how much the selected input variables influence the variance of the fatigue blade load estimations by means of a global sensitivity analysis. Results confirm that the selected ANN architecture seems suitable for this task resulting in small output uncertainties. Furthermore, the sensitivity analysis shows that the turbulence is mainly responsible for the blade load estimation, followed by the wind shear and the wind speed. The contributions of the turbulence length scale, turbulence anisotropy factor and wind veer angle are comparatively low. Comparing three different methods for sensitivity analysis shows that the partial derivative algorithm, Sobol variance decomposition and Shapley effect result in similar sensitivity measures.
Highlights
Wind turbines are typically designed based on reference wind conditions that are provided by the design standard IEC 61400-1 [1]
Recent studies have shown the advantage of replacing aeroelastic simulations with regression models based on Artificial Neural Networks (ANNs), which can be used as surrogate models for fast and efficient wind turbine load assessments
Comparing three different methods for sensitivity analysis shows that the partial derivative algorithm, Sobol variance decomposition and Shapley effect result in similar sensitivity measures
Summary
Wind turbines are typically designed based on reference wind conditions that are provided by the design standard IEC 61400-1 [1]. In order to ensure a turbine’s structural capacity at a specific location, site-specific load assessments need to be carried out using aeroelastic simulations which is a time consuming process and involves complex modeling. Recent studies have shown the advantage of replacing these aeroelastic simulations with regression models, which can be used as surrogate models for fast and efficient load assessments. Once trained on a high-fidelity load simulation database covering a broad range of wind field conditions, the surrogate model can be applied to predict the loads of a specific turbine type for any site with wind climate falling within the range covered by the database.
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