Abstract
Abstract. Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. Here we introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.
Highlights
Estimating extreme loads for wind turbines is made especially difficult by the nonlinear nature of the wind turbine physics combined with the stochastic nature of the wind resources driving the system
We demonstrate the basic mechanics of the algorithm in the context of a study of the effect of the number of peaks, Mpks, used to fit the bin-wise extrapolation distributions (Fi ∼ P (Y < l|xi), above)
Monte Carlo (MC) approaches and maintains contact with the extrapolation methods and thereby allows for iteratively increasing the extrapolation accuracy. This is important because only the extrapolations are able to routinely make estimates of extremely long return period load exceedance probabilities
Summary
Estimating extreme loads for wind turbines is made especially difficult by the nonlinear nature of the wind turbine physics combined with the stochastic nature of the wind resources driving the system. Extreme loads, such as those experienced when a strong gust passes through the rotor or when a turbine has to shut down for a grid emergency, can drive the design of the machine in terms of the material needed to withstand the events. Adaptivity in IS has been introduced in Karamchandani et al (1989) and elsewhere (Melchers, 1990; Mori and Ellingwood, 1993), but does not appear to have broadly taken hold, especially in the context of wind turbine load estimation. We introduce an adaptive IS method for extreme load estimation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
