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We determine the impact of floating turbine motion on turbulence measurements from a four-beam lidar by emulating its scanning configuration and retrievals within atmospheric turbulence boxes. Since the elevation angle of the lidar beams is small and the two bottom lidar beams point closely to the horizontal plane, we also evaluate the turbulence estimation abilities of a two-beam lidar. For the two-beam nacelle lidar, the variance of the individual beams is close to the target -variance and closer than that we compute by reconstructing the -velocity component with the two lidar beams radial velocities. By using floating turbine motion measurements from Hywind, we show that the floating turbine motion impacts turbulence estimations of the nacelle lidar. Roll does not have a clear impact on nacelle-lidar turbulence, whereas both the beam and the -reconstructed variances increase with pitch amplitude.

Rain measurements based on the remote sensing technique play an important role in many fields. In this paper, the DTU-developed continuous-wave SpinnerLidar, deployed on top of a wind turbine, is used to investigate the feasibility of rain measurement. Considering the wind-driven effect on raindrops, a retrieval method is proposed to obtain the rain vector velocity, rain drop size distribution (DSD), and rain intensity from the combined Doppler spectrum of precipitation and wind based on a nacelle-mounted Doppler lidar. To evaluate the performance of the rain identification method, the rain vector velocities and wind speeds identified from the SpinnerLidar are compared with those from the disdrometer and the sonic anemometers, respectively. The results indicate that a nacelle-mounted wind lidar has the ability to observe the rainfall velocity even with low elevation angles of about 20° - 30° of the laser beams. The multi-peak structure produced by raindrops in the Doppler lidar spectrum was reported for the first time, which may be attributed to the wide range of sizes of raindrops and snowflakes, and their different corresponding falling velocities. Additionally, the rain and wind spectra retrieved from the SpinnerLidar are in good agreement with those calculated from the rain drop size and speed distributions from the disdrometer combined with backscattering models, and the probability density function of wind speeds measured by the sonic anemometer, respectively, on both clear and rainy days. Finally, the errors of the DSD and rain intensity estimation related to the rain attenuation effect, rain echo signals beyond the Rayleigh length of continuous-wave lidar, and the two assumptions underlying the proposed method were analyzed and discussed. The results indicated that under typical atmospheric conditions, the absolute value of the error induced by the two assumptions is mainly less than 0.5 m/s, fluctuating between 0.1 to 0.3 m/s.

Large-scale flow structures are vital in influencing the dynamic response of floating wind turbines and wake meandering behind large offshore wind turbines. It is imperative that we develop an inflow wind turbulence model capable of replicating the large-scale and low-frequency wind fluctuations occurring in the marine atmosphere since the current turbulence models do not account well for this phenomenon. Here, we present a method to simulate low-frequency wind fluctuations. This method employs the two-dimensional (2D) spectral tensor for low-frequency, anisotropic wind fluctuations presented by Syed and Mann (2024) to generate stochastic wind fields. The simulation method generates large-scale 2D spatial wind fields for the longitudinal u and lateral v wind components, which can be converted into a frequency domain using Taylor’s frozen turbulence hypothesis. The low-frequency wind turbulence is assumed to be independent of the high-frequency turbulence; thus, a broad spectral representation can be obtained just by superposing the two turbulent wind fields. The method is tested by comparing the simulated and theoretical spectra and co-coherences of the combined low- and high-frequency fluctuations. Furthermore, the low-frequency wind fluctuations can also be subjected to anisotropy. The resulting wind fields from this method can be used to analyze the impact of low-frequency wind fluctuations on wind turbine loads and dynamic response and to study the wake meandering behind large offshore wind farms.

Marine atmospheric turbulence is predominantly characterized by large-scale structures associated with low-frequency wind turbulence. However, conventional turbulence models for assessing wind turbine loads often fall short of adequately integrating these low-frequency wind fluctuations. In this study, we used a model for low-frequency wind fluctuations to generate wind fields representative of offshore wind conditions in the North Sea. The IEA 15 MW reference wind turbine was subjected to three distinct incoming wind fields: high-frequency (3D) turbulence, low-frequency (2D) turbulence combined with high-frequency (3D) turbulence, and 3D turbulence scaled by target or measured standard deviation of wind components. We found that increased damage equivalent loads by incorporating the low-frequency wind fields are only significant for the tower base fore-aft and blade root fore-aft moments. The impact of low-frequency wind turbulence on the damage equivalent loads was more pronounced for below-rated wind speeds. The scaled 3D turbulence yielded the highest lifetime damage equivalent loads, i.e., in the range of 8-18% more than the unscaled 3D turbulence and combined 2D +3D turbulence. This method leads to an overestimation of loads and, hence, more expensive turbines. Incorporating 2D+3D turbulence in wind fields resulted in a 3.8% increase in lifetime damage equivalent loads for the tower base fore-aft and blade root flapwise moments compared to unscaled 3D turbulence. These findings underscore the importance of considering low-frequency wind fluctuations in the turbine design process, especially for a more accurate estimation of loads.

Accurate determination of wind speed offshore is important for the progression of offshore wind energy. Arc-scanning lidars offer precise measurements of both wind speed and direction. They can be placed on a fixed footing, such as a transition piece of a fixed-bottom wind turbine, or on the coast. However, the procedure to derive the wind vector relies on the assumption of homogeneous flow, i.e., that the wind vector is constant along the scanning arc. In this study, we derive a theoretical expression for the wind speed bias due to inhomogeneity in the mean flow. We show that inhomogeneity in the flow will mostly affect the wind component tangential to the arc. The dominating term in the bias equation is equal to the range gate distance times the gradient of the wind speed away from the lidar in the direction along the arc, i.e. crudely, how fast the wind component away from the lidar changes with the scan angle. Atmospheric simulations using the Weather Research and Forecast (WRF) model of flow near mountainous coasts (Madeira Island), where the wind gradients are supposed to be largest, are used to estimate the gradient and, thereby, the bias in a real case. Errors in special situations exceed 50%.

Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 m s−1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 m s−1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.

In moderate to heavy precipitation, raindrops may deteriorate the accuracy of Doppler lidar measurements of the line-of-sight wind velocity because their projected velocity in the beam direction differs greatly from that of air. Therefore, we propose a method for effectively suppressing the adverse effects of rain on velocity estimation by sampling the Doppler spectra faster than the time taken for a raindrop to transit through the beam. By using a special averaging procedure, we can suppress the strong rain signal by sampling the spectrum at 3 kHz. A proof-of-concept field measurement campaign was performed on a moderately rainy day with a maximum rain intensity of 4 mm h−1 using three ground-based continuous-wave Doppler lidars at the Risø campus of the Technical University of Denmark. We demonstrate that the rain bias can effectively be removed by normalizing the noise-flattened 3 kHz sampled Doppler spectra with their peak values before they are averaged down to 50 Hz prior to the determination of the speed. In comparison to the sonic anemometer measurements acquired at the same location, the wind velocity bias at 50 Hz (20 ms) temporal resolution is reduced from up to −1.58 m s−1 for the original raw lidar data to −0.18 m s−1 for the normalized lidar data after suppressing strong rain signals. This reduction in the bias occurs during the minute with the highest amount of rain when the focus distance of the lidar is 103.9 m and the corresponding probe length is 9.8 m. With the smallest probe length, 1.2 m, the rain-induced bias is only present at the period with the highest rain intensity and is also effectively eliminated with the procedure. Thus, the proposed method for reducing the impact of rain on continuous-wave Doppler lidar measurements of air velocity is promising and does not require much computational effort.

We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5-9.5 and 12.5-13.5 m s-1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %-6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %-2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study.

Through numerical simulations and the analysis of field measurements, we investigate the dependence of the accuracy and uncertainty of turbulence estimations on the main features of the nacelle lidars' scanning strategy, i.e., the number of measurement points, the half-cone opening angle, the focus distance and the type of the lidar system. We assume homogeneous turbulence over the lidar scanning area in front of a Vestas V52 wind turbine. The Reynolds stresses are computed via a least-squares procedure that uses the radial velocity variances of each lidar beam without the need to reconstruct the wind components. The lidar-retrieved Reynolds stresses are compared with those from a sonic anemometer at turbine hub height. Our findings from the analysis of both simulations and measurements demonstrate that to estimate the six Reynolds stresses accurately, a nacelle lidar system with at least six beams is required. Further, one of the beams of this system should have a different opening angle. Adding one central beam improves the estimations of the velocity components' variances. Assuming the relations of the velocity components' variances as suggested in the IEC standard, all considered lidars can estimate the along-wind variance accurately using the least-squares procedure and the Doppler radial velocity spectra. Increasing the opening angle increases the accuracy and reduces the uncertainty on the transverse components, while enlarging the measurement distance has opposite effects. All in all, a six-beam continuous-wave lidar measuring at a close distance with a large opening angle provides the best estimations of all Reynolds stresses. This work gives insights on designing and utilizing nacelle lidars for inflow turbulence characterization.

The flow inside and around large offshore wind farms can range from smaller structures associated with the mechanical turbulence generated by wind turbines to larger structures indicative of the mesoscale flow. In this study, we explore the variation in turbulence structures and dominant scales of vertical entrainment above large offshore wind farms located in the North Sea, using data obtained from a research aircraft. The aircraft was flown upstream, downstream, and above wind farm clusters. Under neutrally stratified conditions, there is high ambient turbulence in the atmosphere and an elevated energy dissipation rate compared to stable conditions. The intensity of small-scale turbulence structures is increased above and downstream of the wind farm, and it prevails over mesoscale fluctuations. But in stable stratification, mesoscale flow structures are not only dominant upstream of the wind farm but also downstream. We observed that the vertical flux of horizontal momentum is the main source of energy recovery in large offshore wind farms, and it strongly depends on the magnitude of the length scales of the vertical wind velocity component. The dominant length scales of entrainment range from 20 to ∼60 m above the wind farm in all stratification strengths, and in the wake flow these scales range from 10 to ∼100 m only under near-neutral stratification. For strongly stable conditions, negligible vertical entrainment of momentum was observed even just 2 km downstream of large wind farms. We also observed that there is a significant lateral momentum flux above the offshore wind farms, especially under strongly stable conditions, which suggests that these wind farms do not satisfy the conditions of an "infinite wind farm".