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

Research output: Contribution to conferencePaper


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. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.
Original languageAmerican English
Number of pages15
StatePublished - 2020
EventNAWEA/WindTech 2019 - Amherst, Massachusetts
Duration: 14 Oct 201916 Oct 2019


ConferenceNAWEA/WindTech 2019
CityAmherst, Massachusetts

Bibliographical note

See NREL/JA-5000-77157 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-74409


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


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