Abstract
AbstractThis paper considers the problem of retrospectively de‐trending wind site data when only statistical moments, in the form of 10‐min means and standard deviations in wind speed, are available. Low‐frequency trends present in wind speed data are known to bias fatigue damage estimates, and, hence, removal of their influence is important for accurate fatigue life estimation. When raw data is available, this procedure is straightforward; however, for many sites, significant quantities of data are available, which contain only statistical moments. Additional value is therefore unlocked if de‐trending can also take place in this context. Existing methods, Models 1 and 2, are introduced, and their performance and viability appraised, respectively. A Gaussian process (GP) regression implementation is also developed, which seeks to incorporate characteristics of real trends extracted from raw data into the fitting procedure via an appropriately chosen lengthscale hyperparameter. Results indicate that Model 2, the recommended method in previous work, suffers from fundamental issues, with the implication that it should no longer be used. Model 1 and GP results are shown to be very similar at the turbulence distribution level. This finding is interpreted as a validation of Model 1 and an indication that it may already be performing as well as can be hoped for, given the information available in the current problem formulation. Theoretical overheads associated with GPs, in addition to the performance similarities mentioned above, lead to Model 1 being recommended as the best approach to moment‐based turbulence de‐trending at this time.
Highlights
EPSRC, Grant/Award Numbers: EP/R513349/1, EP/L016680/1 incorporate characteristics of real trends extracted from raw data into the fitting procedure via an appropriately chosen lengthscale hyperparameter
Model 1 (M1) may already be maximising the potential for de-trending on the considered moment data
It is relevant to ask whether the degree chosen in M1 is optimal for the given task and, for any newly developed method, what an appropriate level of smoothing is to best identify the available information in moment data, limited though it may be
Summary
Statistical assessment of site wind resource has important implications for wind farm performance aside from just expected energy yield. The technical definition of trend adopted in the current work is the summation of all Fourier modes present in a wind speed time series with period longer than the capture window This definition coincides with that of Larsen and Hansen.[1] For the case where raw data are available at a site, there are various methods which exist with which different levels of de-trending can be obtained, the most common being a straight line fit to the data. Given that spline orders effectively codify a proportion of smoothing when fitting, it seems pertinent to ask what the correct amount of smoothing might be in this context This question is considered through the development of a Gaussian process (GP) machine learning approach to moment-based de-trending.
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