Abstract

This paper presents a methodology to calculate wind turbine load sensitivities to turbulence parameters through the use of response surfaces. A response surface is a highdimensional polynomial surface that can be calibrated to any set of input/output data and then used to generate synthetic data at a low computational cost. Sobol sensitivity indices (SIs) can then be calculated with relative ease using the calibrated response surface. The proposed methodology is demonstrated by calculating the total sensitivity of the maximum blade root bending moment of the WindPACT 5 MW reference model to four turbulence input parameters: a reference mean wind speed, a reference turbulence intensity, the Kaimal length scale, and a novel parameter reflecting the nonstationarity present in the inflow turbulence. The input/output data used to calibrate the response surface were generated for a previous project. The fit of the calibrated response surface is evaluated in terms of error between the model and the training data and in terms of the convergence. The Sobol SIs are calculated using the calibrated response surface, and the convergence is examined. The Sobol SIs reveal that, of the four turbulence parameters examined in this paper, the variance caused by the Kaimal length scale and nonstationarity parameter are negligible. Thus, the findings in this paper represent the first systematic evidence that stochastic wind turbine load response statistics can be modeled purely by mean wind wind speed and turbulence intensity.

Highlights

  • As the state of the art of wind energy has progressed, the size of the average commercial wind turbine has increased drastically

  • The response surface curve passes through the training data points, demonstrating that the surface accurately models the trends in the training data

  • This paper presents a methodology to calibrate response surfaces to wind turbine response statistics data

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Summary

Introduction

As the state of the art of wind energy has progressed, the size of the average commercial wind turbine has increased drastically. This increase in wind turbine size is primarily driven by the increased power production that large rotors yield. The increase in wind turbine size has increased complications in terms of transportation, construction, and design. As the wind turbine size has increased, the importance of adequately designing the system has increased. The wind turbine models and training data used in this paper were available from a previous project. Detailed descriptions of the wind turbine model can be found in [15]

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