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.
Original language | American English |
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Article number | Article No. 012006 |
Number of pages | 12 |
Journal | Journal of Physics: Conference Series |
Volume | 1452 |
Issue number | 1 |
DOIs | |
State | Published - 3 Mar 2020 |
Event | North 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 2019 → 16 Oct 2019 |
Bibliographical note
See NREL/CP-5000-74409 for preprintNREL Publication Number
- NREL/JA-5000-77157
Keywords
- covariance
- transient atmospheric event
- wake steering
- wind plant controls