Abstract
Super-large-scale particle image velocimetry and flow visualization with natural snowfall is used to collect and analyse multiple datasets in the near wake of a 2.5 MW wind turbine. Each dataset captures the full vertical span of the wake from a different perspective. Together, these datasets compose a three-dimensional picture of the near-wake flow, including the effect of the tower and nacelle and the variation of instantaneous wake expansion in response to changes in turbine operation. A region of high-speed flow is observed directly behind the nacelle, and a region of low-speed flow appears behind the tower. Additionally, the nacelle produces a region of enhanced turbulence in its wake while the tower reduces turbulence near the ground as it breaks up turbulent structures in the boundary layer. Analysis of the instantaneous wake behavior reveals variations in wake expansion – and even periods of wake contraction – occurring in response to changes in angle of attack and blade pitch gradient. This behaviour is found to depend on the region of operation of the turbine. These findings can be incorporated into wake models and advanced control algorithms for wind farm optimization and can be used to validate wind turbine wake simulations.
Highlights
Understanding the wind turbine wake is crucial for improving the efficiency of wind farms, as wake loss can cause power losses of 10-20% in large wind farms [1] and can increase fatigue loading on downwind turbines [2]
The behavior in the near wake (1-4 rotor diameters downstream) influences far-wake development, as the breakdown of near-wake coherent structures enhances mixing and recovery [3], and the interaction between the vortices shed from the hub and blades affects meandering [4]
A number of studies have investigated the regulation of wake velocity deficit using blade pitch and generator torque to maximize wind farm power generation [7, 8]
Summary
Understanding the wind turbine wake is crucial for improving the efficiency of wind farms, as wake loss can cause power losses of 10-20% in large wind farms [1] and can increase fatigue loading on downwind turbines [2]. A number of studies have investigated the regulation of wake velocity deficit using blade pitch and generator torque to maximize wind farm power generation [7, 8] Though these studies have yielded many useful results, they were all conducted using either numerical simulation or conventional field measurement techniques such as LiDAR, which have substantial limitations in resolving the complex interaction between the turbulent atmospheric flow and the turbine structure in the near wake. Super-large-scale particle image velocimetry (SLPIV) and flow visualization, first implemented by Hong et al [10], provides high-resolution velocity fields for atmospheric flows using natural snowflakes as flow tracers This technique was first validated and applied to preliminary utility-scale wind turbine wake measurements by Hong et al [9] and Toloui et al [10].
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