Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation. (January 2020)

• Record Type: Journal Article
• Authors: Hamilton, Nicholas
• Is Part Of: Journal of physics. Volume 1452(2020)
• Publication Date: 2020-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1452/1/012006
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.