Wind site turbulence de‐trending using statistical moments: Evaluating existing methods and introducing a Gaussian process regression approach

Abstract

Abstract This 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.

Keywords

• de‐trending
• Gaussian process
• site conditions
• turbulence
• wind energy