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Analytical wake models provide a computationally efficient means to predict velocity distributions in wind turbine wakes in the atmospheric boundary layer (ABL). Most existing models are developed for neutral atmospheric conditions and correspondingly neglect the effects of buoyancy and Coriolis forces that lead to veer, i.e., changes in the wind direction with height. Both veer and changes in thermal stratification lead to lateral shearing of the wake behind a wind turbine, which affects the power output of downstream turbines. Here we develop an analytical engineering wake model for a wind turbine in yaw in ABL flows including Coriolis and thermal stratification effects. The model combines the new analytical representation of ABL vertical structure based on coupling Ekman and surface layer descriptions developed in Narasimhan et al. [Boundary Layer Meteorol. 190, 16 (2024)] with the vortex sheet-based wake model for yawed turbines proposed in Bastankhah et al. [J. Fluid Mech. 933, A2 (2022)], as well as a new method to predict the wake expansion rate based on the Townsend-Perry logarithmic scaling of streamwise velocity variance. The proposed wake model's predictions show good agreement with large-eddy simulation results, capturing the effects of wind veer and yawing, including the curled and sheared wake structures across various states of the ABL, ranging from neutrally to strongly stably stratified atmospheric conditions. The model significantly improves power loss predictions from wake interactions, especially in strongly stably stratified conditions where wind veer effects dominate.
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Wake Effect Calibration in Wind Power Systems with Adaptive Sampling based Optimization
The calibration of the wake effect in wind turbines is computationally expensive and with high risk due to noise in the data. Wake represents the energy loss in downstream turbines, and characterizing it is essential to design wind farm layout and control turbines for maximum power generation. With big data, calibrating the wake parameters is a derivative-free optimization that can be computationally expensive. But with stochastic optimization combined with variance reduction, we can reach robust solutions by harnessing the uncertainty through two sampling mechanisms: the sample size and the sample choices. We do the former by generating a varying number of samples and the latter using the variance-reduced sampling methods.
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- Award ID(s):
- 1741166
- PAR ID:
- 10384640
- Date Published:
- Journal Name:
- IIE Annual Conference. Proceedings
- Page Range / eLocation ID:
- 43-48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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