The robust estimation of wind turbine design loads for service lifetimes of 30 to 50 years that are based on limited field measurements is a challenging problem. Estimating the long-term load distribution involves the integration of conditional distributions of extreme loads over the mean wind speed and turbulence intensity distributions. However, the accuracy of the statistical extrapolation can be sensitive to both model and sampling errors. Using measured inflow and structural data from the Long Term Inflow and Structural Test (LIST) program, this paper presents a comparative assessment of extreme loads using three distributions: namely, the Gumbel, Weibull and Generalized Extreme Value distributions. The paper uses L-moments, in place of traditional product moments, with the purpose of reducing the sampling error. The paper discusses the effects of modeling and sampling errors and highlights the practical limitations of extreme value theory.
Probabilistic Analysis of LIST Data for the Estimation of Extreme Design Loads for Wind Turbine Components*†
Contributed by the Solar Energy Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received by the ASME Solar Energy Division, February 27, 2003; final revision, July 15, 2003. Associate Editor: D. Berg.
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Pandey, M. D., and Sutherland, H. J. (November 26, 2003). "Probabilistic Analysis of LIST Data for the Estimation of Extreme Design Loads for Wind Turbine Components." ASME. J. Sol. Energy Eng. November 2003; 125(4): 531–540. https://doi.org/10.1115/1.1626128
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