Lidar Wind Preview Quality Estimation for Wind Turbine Control

Lidar-assisted wind turbine control has been proven to be beneficial for wind turbines. This technology uses the preview of rotor-effective wind speed obtained from lidar measurements in front of the wind turbine. The wind preview allows the controller to react to the disturbance prior to the impact on the turbine. However, the quality of the wind preview provided by a lidar is constantly changing mainly with atmospheric conditions and usually only the lower frequency components of the signal can be used for control. Thus, it is necessary to estimate the wind preview quality online and accordingly adjust the filter parameters to remove the uncorrelated information and to schedule controller parameters. Previously, the wind preview quality during short field testing campaigns has been identified offline prior to using the lidar signal for control. This was done with a frequency-based correlation study by comparing the rotor-effective wind speed estimated from turbine data to the one provided by the lidar. An online application of the frequency-based correlation study however is hard to implement due to the limitation of the estimation from turbine data, the amount of data necessary, and the sensitivity of the frequency-based method itself. In this paper, we develop a new statistic-based method to estimate the wind preview quality by only using the data from a pulsed lidar system without the need of wind turbine data. The method is assessed using simulated lidar measurement scanning an unfrozen wind field. The results show that the method is able to distinguish the preview quality under different wind evolution conditions.