In:
Wind Energy Science, Copernicus GmbH, Vol. 3, No. 2 ( 2018-07-11), p. 475-487
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.
Type of Medium:
Online Resource
ISSN:
2366-7451
DOI:
10.5194/wes-3-475-2018
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2846783-8
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