LiDAR-based wind speed measurements have seen a significant increase in interest in wind energy. However, reconstruction of wind speed vector from a LiDAR-measured radial wind speed is still a challenge. Furthermore, for extensive application of LiDAR technology, it can be used as a means to validate simulation and analytical models. To that end, this study employed scanning Doppler LiDAR for assessment of wind fields at an offshore site and compared Weather Research and Forecasting (WRF)-based mesoscale simulations and several wake models with the measurements. Firstly, the effect of carrier-to-noise-ratio (CNR) and data availability on the quality of scanning LiDAR measurements was evaluated. Analysis of vertical profiles show that the average wind speed is higher for wind blowing from the sea than that blowing from the land. Furthermore, profiles obtained from the WRF simulation also show a similar tendency in the LiDAR measurements in general, though it overestimates the wind speeds at higher altitudes. A method for reconstruction of wind fields from plan-position indicator (PPI) and range height indicator (RHI) scans of LiDAR-measured line of sight velocities was then proposed and first used to investigate the effect of coastal terrain. An internal boundary layer with strong shear could be observed to develop from the coastline. Finally, the flow field around wind turbine was measured using PPI scan and used to validate wake models.

This study proposes a microscale flow model to estimate mean wind speed, fluctuating wind speed and wind direction over complex terrain considering the effects of topography, atmospheric stability, and turbine wakes. Firstly, the effect of topography is considered using Computational Fluid Dynamics (CFD). Next, a mesoscale model is presented to account for the effect of atmospheric stability. The effect of turbine wakes on the mean and fluctuating wind speeds are then represented by an advanced wake model. The model is validated using the measurement data of a wind farm located in the North of Japan. The measured wind data by Lidar at a reference height are horizontally extrapolated to a nearby met mast hub height and validated by a cup anemometer. Moreover, a novel averaging method is proposed to calculate a directional equivalent Monin–Obukhov length scale to account for the effect of atmospheric stability. Finally, the measured wind data at the reference height are vertically extrapolated and validated at the lidar location. The predicted mean and fluctuating wind speeds show good agreement with the measurements.

In this study, a new motion compensation algorithm was proposed and verified by using numerical simulation. Compensated horizontal mean wind speed by using conventional method shows good agreement with reference wind speed regardless of the motion of the floater. However, turbulence intensity is always overestimated. The overestimation is more significant when the maximum pitch angle of the floater motion is larger. When proposed method is used, the overestimation of the turbulent intensity is improved and estimated turbulent intensity shows better agreement with reference value. There still remains underestimation of the turbulence intensity with the bias of -1.1%. This is probably caused by the low sampling frequency in LIDAR measurement and further research is needed to model the high frequency component of the wind speed for LIDAR measurement.

An advanced pitch controller is proposed for the load mitigation of wind turbines. This study focuses on the nacelle acceleration feedback control and lidar-based feedforward control, and discusses how these controllers contribute to reduce the load on wind turbines. The nacelle acceleration feedback control increases the damping ratio of the first mode of wind turbines, but it also increases the fluctuation in the rotor speed and thrust force, which results in the optimum gain value. The lidar-based feedforward control reduces the fluctuation in the rotor speed and the thrust force by decreasing the fluctuating wind load on the rotor, which reduces the fluctuating load on the tower. The combination of the nacelle acceleration feedback control and the lidar-based feedforward control successfully reduces both the response of the tower first mode and the fluctuation in the rotor speed at the same time.

For the design of floating offshore wind turbines (FOWT), all the load cases need to be calculated by using a coupled model of wind turbine and platform, while the uncoupled approach will help to reduce the number of simulations for the design and optimization of floating offshore wind turbines. In this study, the effects of the wind and wave actions on the tower loading of a FOWT were investigated and an uncoupled approach for the load calculation was proposed and verified by comparing with the result of coupled simulation. First, the effect of elastic platform was considered by tuning the Young’s modulus of the tower material when rigid platform model was used in the analysis. The effect of wind and wave actions on the loading of the tower was then investigated. It was found that the difference of the wind load between fixed and floating wind turbines is observed only in the mean component and can be predicted by considering the hydrostatic stiffness of the platform and mooring stiffness. The standard deviation of the fore-aft tower moment increased as the significant wave heights increased when the mean wind speeds and peak wave periods were fixed. This is caused by the increase of the inertia force induced by the pitch and surge motions of the platform and the increase of the fluctuating pitch angle. On the other hand, the standard deviation of the fore-aft tower moment decreased as the peak periods increased when the mean wind speeds and significant wave heights were fixed. The increase of the peak period caused the decrease of the pitch and surge accelerations of the platform and results in the decrease of the inertia force. Finally, the tower loading in extreme sea states during power production was carried out by using the proposed uncoupled approach and the results showed good agreement with those by the coupled approach, and the simulation time was reduced to 1/40.