Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Abstract

Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

“…The summertime offshore wind veer conditions are similar to the nighttime stable conditions found onshore in Walter et al (2009). Veer is of particular interest with respect to wind turbine wake propagation (Bodini et al, 2017;Churchfield & Sirnivas, 2018) and wakes impact power production the most at wind speeds in region 2. The coupling of the strong veer in summertime with low dissipation will result in long-propagating but skewed wakes, impacting power production and turbulent loads on downwind turbines.…”

“…An annual cycle also emerges in wind veer, another important atmospheric variable that affects the structure of wind turbine wakes (Abkar et al, 2018;Bodini et al, 2017;Churchfield & Sirnivas, 2018). We calculate wind veer as the difference in 2-min average wind direction, calculated from the lidar, between 40 and 200 m ASL, typical vertical limits for the rotor of offshore wind turbines.…”

Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence

“…The annual cycle of turbulence dissipation rate offshore is more influenced by the wind-land interaction rather than the seasonal cycle itself. An annual cycle also emerges in wind veer, another important atmospheric variable which affects the structure of wind turbine wakes (Bodini et al, 2017;Abkar et al, 2018;Churchfield & Sirnivas, 2018). We calculate wind veer as the difference in 2-minute average wind direction, retrieved from the lidar, between 40 m and 200 m ASL, which represent typical vertical limits for the rotor of modern offshore wind turbine models.…”

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

“…While the former might not be representative for the local wind direction at specific turbine locations due to spatial variability of the wind field, the latter is recorded with a low sampling rate of 10 minutes and it might differ from the incoming wind direction in presence of a significant wind veer. 29 Although cases with evident wind veer are excluded for this data analysis, even small errors in the estimate of the wake direction can lead to inaccurate characterizations of the wake recovery.…”