LIDAR Wind Speed Measurements of Evolving Wind Fields

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems that are designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed the validity of physicist G.I. Taylor’s 1938 frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor’s hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations using the National Renewable Energy Laboratory’s (NREL’s) 5-megawatt turbine model to create a more realistic measurement model. A simple model of wind evolution was applied to a frozen wind field that was used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements were also evaluated using a large eddy simulation (LES) of a stable boundary layer that was provided by the National Center for Atmospheric Research. The LIDAR measurement scenario investigated consists of a hub-mounted LIDAR that scans a circle of points upwind of the turbine in order to estimate the wind speed component in the mean wind direction. Different combinations of the preview distance that is located upwind of the rotor and the radius of the scan circle were analyzed. It was found that the dominant source of measurement error for short preview distances is the detection of transverse and vertical wind speeds from the line-of-sight LIDAR measurement. It was discovered in previous studies that, in the absence of wind evolution, the dominant source of error for large preview distances is the spatial averaging caused by the LIDAR’s sampling volume. However, by introducing wind evolution, the dominant source of error for large preview distances was found to be the coherence loss caused by evolving turbulence. Different measurement geometries were compared using the bandwidth for which the measurement coherence remained above 0.5 and also the area under the measurement coherence curve. Results showed that, by increasing the intensity of wind evolution, the measurement coherence decreases. Using the coherence bandwidth metric, the optimal preview distance for a fixed-scan radius remained almost constant for low and moderate amounts of wind evolution. For the wind field with the simple wind evolution model introduced, the optimal preview distance for a scan radius of 75% blade span (47.25 meters) was found to be 80 meters. Using the LES wind field, the optimal preview distance was 65 meters. When comparing scan geometries using the area under the coherence curve, results showed that, as the intensity of wind evolution increases, the optimal preview distance decreases.