Lidar detection of wakes for wind turbine and farm control

In the recent years the lidar technology has found its way into the wind energy business.

The remote sensing abilities of lidars makes them a very attractive alternative to traditional anemometry, like cup or sonic anemometers, which led to a wide variety of applications ranging from site assessment and power curve validation over lidar-assisted control to complex flow measurements. However, when using lidar measurements it has to be kept in mind that a lidar is only able to measure the radial component of the wind velocity vector. Thus a single lidar system can resolve one component of the usually 3D wind vector (cyclops dilemma). To overcome this limitation either three spatially separated systems focusing on the same measurement point are required or assumptions during the wind field reconstruction have to be made. A very popular assumption is that of homogeneous inflow conditions and while this is a good approximation over flat terrain, it can be heavily violated under complex conditions or in wake situations.

Project description:

In this project a commercial available lidar by Windar Photonics A/S, called WindEYE, will be used. It uses line-of-sight (LOS) measurement at two different locations in front of the turbine to derive a misalignment between the current turbine yaw position and the wind direction. It is assumed that the flow is homogeneous. The basic problem is this assumption is violated within wakes, meaning that a reduced wind speed due to a wake in one of the beams will be interpreted as a turbine yaw misalignment.

The aim of this project is to answer how the lidar can detect wakes in the inflow. Hypothesis is that the lidar can measure enhanced turbulence levels, and that this enables the system to determine whether one or more beams are measuring inside a wake. However, lidar turbulence measured is affected by the relatively long effective measurement volume. The lidar averages wind speeds within the volume and acts as a low-pass filter attenuating high-frequency turbulence. The averaged turbulence manifests itself in the width of the Doppler peak of the lidar, so that this width is a measure of the small-scale turbulence with length scales smaller than the effective average volume. Specifically, it has been shown that the width of the Doppler spectrum of a continuous-wave lidar is proportional to the turbulence within the probe volume of the instrument. At the same time, it is known that small-scale turbulence, which would be particularly responsible for increasing the Doppler spectrum width, is prevalent in wakes. This fact can be utilized as the basis of a wake detection algorithm.


The wake detection algorithm can be directly applied to the WindEYE and give it a more competitive edge by increasing yaw misalignment accuracy. Additionally, information about the position of wakes can be used for individual wind turbine and entire wind farm control. This includes sector management where turbines are shut down or derated for certain wind directions where wakes are expected (and subsequently) to reduce mechanical loads on the turbine. Wake detection could make sector management more efficient. Another technique is wind farm management where turbines upstream pitch their blades to reduce the extracted power and mitigate the severity of the wake (derating). Wake detection can guarantee that the derating only takes place when it is actually necessary. A more radical approach is wake deflection by yawing the upstream turbine and essentially pushing the wake to the side in order not to hit a downstream turbine.