Preprints
https://doi.org/10.5194/wes-2021-68
https://doi.org/10.5194/wes-2021-68

  23 Jul 2021

23 Jul 2021

Review status: this preprint has been withdrawn by the authors.

Approaches for predicting wind turbine hub-height turbulence metrics

Hannah Livingston1, Nicola Bodini2, and Julie K. Lundquist2,3 Hannah Livingston et al.
  • 1Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado, USA
  • 2National Renewable Energy Laboratory, Golden, Colorado, USA
  • 3Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA

Abstract. Hub-height turbulence is essential for a variety of wind energy applications, ranging from wind plant siting to wind turbine control strategies. Because deploying hub-height meteorological towers can be a challenge, alternative ways to estimate hub-height turbulence are desired. In this paper, we assess to what degree hub-height turbulence can be estimated via other hub-height variables or ground-level atmospheric measurements in complex terrain, using observations from three meteorological towers at the Perdigão and WFIP2 field campaigns. We find a large variability across the three considered towers when trying to model hub-height turbulence intensity (TI) and turbulence kinetic energy (TKE) from hub-height or near-surface measurements of either wind speed, TI, or TKE. Moreover, we find that based on the characteristics of the specific site, atmospheric stability and upwind fetch either determine a significant variability in hub-height turbulence or are not a main driver of the variability in hub-height TI and TKE. Our results highlight how hub-height turbulence is simultaneously sensitive to numerous different factors, so that no simple and universal relationship can be determined to vertically extrapolate turbulence from near-surface measurements, or model it from other hub-height variables when considering univariate relationships. We suggest that a multivariate approach should instead be considered, possibly leveraging the capabilities of machine learning nonlinear algorithms.

This preprint has been withdrawn.

Hannah Livingston et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer comment on wes-2021-68', Anonymous Referee #1, 17 Aug 2021
  • RC2: 'Comment on wes-2021-68', Anonymous Referee #2, 22 Aug 2021
  • EC1: 'Comment on wes-2021-68', Andrea Hahmann, 05 Sep 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer comment on wes-2021-68', Anonymous Referee #1, 17 Aug 2021
  • RC2: 'Comment on wes-2021-68', Anonymous Referee #2, 22 Aug 2021
  • EC1: 'Comment on wes-2021-68', Andrea Hahmann, 05 Sep 2021

Hannah Livingston et al.

Hannah Livingston et al.

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This preprint has been withdrawn.

Short summary
In this paper, we assess whether hub-height turbulence can easily be quantified from either other hub-height variables or ground-level measurements in complex terrain. We find a large variability across the three considered locations when trying to model hub-height turbulence intensity and turbulence kinetic energy. Our results highlight the nonlinear and complex nature of atmospheric turbulence, so that more powerful techniques should instead be recommended to model hub-height turbulence.