Wind turbine wakes, the regions of slower and more turbulent air behind turbines, lead to power losses up to 40% and increased structural loading on downwind turbines within a wind farm. To mitigate these detrimental effects, improved understanding of wake behavior is required. However, modeling wind turbine flows in the laboratory or in simulations is hindered by the wide range of relevant scales and the complexity of atmospheric flow and turbine operation. Because of these limitations, field scale studies are essential. Conventional field scale flow measurement techniques such as lidar and anemometers have limited spatio-temporal resolution, inhibiting their ability to capture the highly dynamic and heterogeneous behaviors characteristic of wind turbine wakes. To address these shortcomings, super-large-scale flow imaging with natural snowfall is used to analyze the flow in the wake of utility-scale wind turbine. The current work focuses on the impact of constantly-changing atmospheric conditions and dynamic turbine operation on the near wake, the region within four rotor diameters downstream of the turbine. The flow in this region significantly impacts wake development downstream, including meandering, mixing, and recovery. The current research investigates the effect of coherent vortical structures in the inflow, the impact of the turbine nacelle and support tower on the near wake, dynamic wake modulation, and the interaction between the wake and the ground surface. Dynamic wake modulation, the large-scale motion of the wake in response to changes in incoming flow and turbine operation, is shown to substantially enhance mixing between the wake and the surrounding flow. The findings of this research have important implications for wind farm design, layout, and controls. New understanding of wind turbine wake behaviors can be incorporated into simplified models used to optimize wind farms and evaluate their impacts on their surroundings. Improving the accuracy of these models can increase efficiency and reduce power production uncertainty. Additionally, advanced control algorithms to minimize wake losses can be designed based on the presented relationships between turbine operational parameters readily available to the controller and wake behaviors. These direct connections pave the way for more precise wake prediction and control under real operating conditions.
University of Minnesota Ph.D. dissertation. July 2021. Major: Mechanical Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); xxiv, 166 pages.
The effect of dynamic operation and incoming flow on the wake of a utility-scale wind turbine.
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