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Research Papers

Assessment of Load Extrapolation Methods for Wind Turbines

[+] Author and Article Information
Henrik Stensgaard Toft

Department of Civil Engineering, Aalborg University, Sohngaardsholmsvej 57, 9000 Aalborg, Denmark

John Dalsgaard Sørensen

Department of Civil Engineering, Aalborg University, Sohngaardsholmsvej 57, 9000 Aalborg, Denmark; Wind Energy Division, Risø-DTU, Frederiksborgvej 399, 4000 Roskilde, Denmark

Dick Veldkamp

 Vestas Technology R&D, Global Research, c/o Vestas Central Europe, P.O. Box 208, 6800 AE Arnhem, The Netherlands

J. Sol. Energy Eng 133(2), 021001 (Feb 23, 2011) (8 pages) doi:10.1115/1.4003416 History: Received January 18, 2010; Revised October 29, 2010; Published February 23, 2011; Online February 23, 2011

In the present paper, methods for statistical load extrapolation of wind-turbine response are studied using a stationary Gaussian process model, which has approximately the same spectral properties as the response for the out-of-plane bending moment of a wind-turbine blade. For a Gaussian process, an approximate analytical solution for the distribution of the peaks is given by Rice. In the present paper, three different methods for statistical load extrapolation are compared with the analytical solution for one mean wind speed. The methods considered are global maxima, block maxima, and the peak over threshold method with two different threshold values. The comparisons show that the goodness of fit for the local distribution has a significant influence on the results, but the peak over threshold method with a threshold value on the mean plus 1.4 standard deviations generally gives the best results. By considering Gaussian processes for 12 mean wind speeds, the “fitting before aggregation” and “aggregation before fitting” approaches are studied. The results show that the fitting before aggregation approach gives the best results.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Normalized power spectrum for blade out-of-plane bending moment at 15 m/s

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Figure 2

Skewness and kurtosis for blade out-of-plane bending moment

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Figure 3

Normalized power spectrum for blade out-of-plane bending moment at 15 m/s

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Figure 4

Irregularity factor α for blade out-of-plane bending moment

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Figure 5

Comparison of local distributions with Rice, 100 time series, and peaks extracted by global maxima; operation at 15 m/s

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Figure 6

Comparison of global maxima, block maxima, and peak over threshold using Weibull distribution and 50 year return period; operation at 15 m/s (1=Rice solution)

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Figure 7

Comparison of local Weibull distribution with Rice and 100 time series; operation at 15 m/s

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Figure 8

Comparison for different return periods, Weibull distribution, and 100 time series; operation at 15 m/s (1=Rice solution)

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Figure 9

Comparison of “fitting before aggregation” and “aggregation before fitting,” fit: Weibull distribution and 50 year return period; operation at all wind speeds (1=Rice solution)

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Figure 10

Comparison for different return periods, fit: Weibull distribution, Nsim=100 for “fitting before aggregation,” and Nsim=1200 for “aggregation before fitting;” operation at all wind speeds (1=Rice solution)

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