Search Results (7)

We propose an empirical motion compensation algorithm for a better turbulence intensity (TI) measurement by Floating LiDAR systems (FLSs) with a newly introduced motion parameter, the significant tilt angle ${\theta }_{\alpha ,1/3}$, using four datasets from three different FLSs in Japan. The parameter was compared to other environmental parameters; it was confirmed to well represent various types of buoy motion. A sensitivity assessment was conducted for the error of the FLS’s standard deviation of wind speed to the buoy motion. The strong correlation obtained by the assessment suggests that the error of the FLS TI is dominated by the motion and that it is possible to offset the error by applying the relationship back to the measurement. The corrected TI shows good agreement with that of a reference fixed vertical LiDAR (VL). Moreover, the similarity of the relationships for the same type of VL mounted on different buoys implies that the correction may be VL-specific rather than FLS-specific, and, therefore, universal regardless of the FLS type. The successful validation suggests that the correction based on ${\theta }_{\alpha ,1/3}$ can be applied not only to the future campaign but also to those performed in the past to revitalize numerous existing FLS datasets. Full article

While floating wind lidars provide reliable and cost-effective measurements, these measurements may be inaccurate due to the motion of the installation platforms. Prior studies have not distinguished between systematic errors associated with lidars and errors resulting from motion. This study will fill this gap by examining the impact of platform motion on two types of profiling wind lidar systems: the pulsed WindCube V1 (Leosphere) and the continuous-wave ZephIR 300 (Natural Power). On a moving hexapod platform, both systems were subjected to 50 controlled sinusoidal motion cases in different degrees of freedom. Two reference lidars were placed at a distance of five meters from the platform as reference lidars. Motion-induced errors in mean wind speed and turbulence intensity estimation by lidars are analyzed. Additionally, the effectiveness of a motion correction approach in reducing these errors across various scenarios is evaluated. The results indicate that presence of rotational motion leads to higher turbulence intensity (TI) estimation by moving lidars. The absolute percentage error between lidars is the highest when lidars are exposed to yaw and heave motion and is the lowest when exposed to surge motion. The correlation between lidars, though it is the lowest in the presence of pitch, yaw, and heave motion. Furthermore, applying motion compensation can compensate the correlation drop and erroneous TI estimation. Full article

Floating LiDAR systems (FLSs) may replace conventional offshore met masts, and they have been developed well in Europe. However, before using them in Japan, we must determine whether they demonstrate the same performance under the unique East-Asian meteorological and oceanographic conditions. Therefore, herein, we investigate the performance of FLSs by focusing on the differences among models. Four independent wind datasets from three FLSs were simultaneously verified against a reference met mast and vertical LiDAR at a Japanese site. The data availability was confirmed to vary from 62.7 to 98.0% over the period at 63 m. This was strongly affected by the system availability of the buoy and LiDAR, suggesting that buoy system robustness is key to better campaigns with higher data availability. The 10 min averaged wind speed and direction largely satisfied the Carbon Trust’s key performance indicators, with a low sensitivity to wave conditions depending on the buoy shape. The standard deviation of the wind speed and turbulence intensity had poorer accuracy than that of the 10 min averaged statistics because of the wave-induced buoy motion, especially for small buoys. In short, this paper provides an overview of a measurement by FLS in Japan. Also, the unique verification with multiple units suggests the need for a low-motion buoy or motion compensation to improve the measurement accuracy of the turbulence component. Full article

This study examines how motion influences turbulent velocity fluctuations utilizing measurements obtained from a wind lidar profiler. Onshore tests were performed using a WindCube v2.1 lidar, which was mobile and mounted on a hexapod to simulate buoy motion. Additionally, a fixed WindCube v2.1 lidar was used as a reference during these tests. To assess the motion-induced effects on velocity fluctuations measured by floating lidar systems, the root-mean-square error (RMSE) of velocity fluctuations obtained from the fixed and mobile lidars was calculated. A comprehensive wind dataset spanning 22.5 h was analyzed, with a focus on regular motions involving single-axis rotations and combinations of rotations around multiple axes. The investigation of single-axis rotations revealed that the primary influencing factor on the results was the alignment between the tilt direction of the mobile lidar and the wind direction. The highest RMSE values occurred when the tilt of the mobile lidar leans in the wind direction, resulting in pitch motion, whereas the lowest RMSE values were observed when the tilt of the mobile lidar leans perpendicular to the wind direction, resulting in roll motion. Moreover, the addition of motion around extra axes of rotation was found to increase RMSE. Full article

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating $ZephI{R}^{\mathrm{TM}}$ 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed. Full article

Floating LIDAR systems (FLS) are a cost-effective way of surveying the wind energy potential of an offshore area. However, as turbulence intensity estimates are strongly affected by wave-induced buoy motion, it is essential to correct them. In this study, we quantify the turbulence intensity measurement error of a WindCube v2^® mounted on a 12-ton anchored buoy as a function of met-ocean conditions, and we construct a subsequently applied correction method suitable for 10-min wind LIDAR data storage. To this end, we build a model to simulate the effect of buoyancy movements on the LIDAR’s wind measurements. We first apply the model to understand the mechanisms responsible for the wind LIDAR measurement error. The effect of the buoy’s rotational and translational motions on the radial wind speed measurements of the individual beams is first studied. Second, the temporality induced by the LIDAR operation is taken into account; the effect of motion subsampling and the interaction between the different measurement beam positions. From this model, a correction method is developed and successfully applied to a 13-week experimental campaign conducted off the shores of Fécamp (Normandie, France) involving the buoy-mounted WindCube v2^® compared with cup anemometers from a met mast and a fixed WindCube v2^® on a platform. The correction improves the linear regression against the fixed LIDAR turbulence intensity measurements, shifting the offset from ~0.03 to ~0.005 without post-processing the remaining peaks. Full article

This article presents the Ferry Lidar Experiment, which is one of the NEWA Experiments, a set of unique flow experiments conducted as part of the New European Wind Atlas (NEWA) project. These experiments have been prepared and conducted to create adequate datasets for mesoscale and microscale model validation. For the Ferry Lidar Experiment a Doppler lidar instrument was placed on a ferry connecting Kiel and Klaipeda in the Southern Baltic Sea from February to June 2017. A comprehensive set of all relevant motions was recorded together with the lidar data and processed in order to obtain and provide corrected wind time series. Due to the existence of the motion effects, the obtained data are essentially different from typical on-site data used for wind resource assessments in the wind industry. First comparisons show that they can be well related to mapped wind trajectories from the output of a numerical weather prediction model showing a reasonable correlation. More detailed validation studies are planned for the future. Full article