Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation

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Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Results in the current study emphasize the scope and intended range of wake models used for wind plant control and suggest that either models be defined to account for the transient nature of the atmosphere, or that their validation and application be geared to stationary atmospheric conditions.

Original languageAmerican English
Article numberArticle No. 012006
Number of pages12
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 3 Mar 2020
EventNorth American Wind Energy Academy, NAWEA 2019 and the International Conference on Future Technologies in Wind Energy 2019, WindTech 2019 - Amherst, United States
Duration: 14 Oct 201916 Oct 2019

Bibliographical note

See NREL/CP-5000-74409 for preprint

NREL Publication Number

  • NREL/JA-5000-77157


  • covariance
  • transient atmospheric event
  • wake steering
  • wind plant controls


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