Wind turbine wake characterization using long-range Doppler lidar

Wind turbines extract energy from the freestream flow, resulting in a waked region behind the rotor which is characterized by reduced wind speed and increased turbulence. The velocity deficit in the wake diminishes with distance, as faster-moving air outside is gradually entrained. In a concentrated group of turbines, then, downwind machines experience very different inflow conditions compared to those in the front row. As utility-scale turbines rarely exist in isolation, detailed knowledge of the mean flow and turbulence structure inside wakes is needed to correctly model both power production and turbine loading at modern wind farms.

To this end, the Turbine Wake and Inflow Characterization Study (TWICS) was conducted in the spring of 2011 to determine the reduction in wind speeds downstream from a multi-MW turbine located at the National Renewable Energy Laboratory's National Wind Technology Center (NWTC) near Boulder, Colorado. Full-scale measurements of wake dynamics are hardly practical or even possible with conventional sensors, such as cup anemometers mounted on meteorological (met) masts. Accordingly, the High Resolution Doppler Lidar (HRDL) developed by the National Oceanic and Atmospheric Administration's Earth System Research Laboratory was employed to investigate the formation and propagation of wakes under varying levels of ambient wind speed, shear, atmospheric stability, and turbulence. HRDL remotely senses line-of-sight wind velocities and has been used in several previous studies of boundary layer aerodynamics. With a fully steerable beam and a maximum range up to about 5 km, depending on atmospheric conditions, HRDL performed a comprehensive survey of the wind flow in front of and behind the turbine to study the shape, meandering, and attenuation of wakes.

Due in large part to limited experimental data availability, wind farm wake modeling is still subject to an unacceptable amount of uncertainty, particularly in complex terrain. Here, analytical techniques are developed to distinguish wakes from the background variability, and moreover, wakes are then classified by width, height, length, and velocity deficit based on atmospheric stability and inflow conditions. By integrating these advanced observational capabilities with innovative approaches to atmospheric modeling, this work will help to improve simulation tools used to quantify power loss and fatigue loading due to wake effects, thereby aiding the optimization of wind farm layouts.