Lidar systems installed on the nacelle of wind turbines can provide a preview of incoming turbulent wind. Lidar-assisted control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feedforward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes, unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include realistic longitudinal coherence, thus avoiding assuming Taylor's frozen hypothesis. The Reference Open-Source Controller (ROSCO) by NREL is used as the baseline feedback-only controller. A reference lidar-assisted controller is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LAC is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (up to 40 %), tower fore–aft bending moment (up to 16.7 %), and power variation (up to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence parameters, and the mean wind speed.

Feedforward blade pitch control is one of the most promising lidar-assisted control strategies due to its significant improvement in rotor speed regulation and fatigue load reduction. A high-quality preview of the rotor-effective wind speed is a key element of control benefits. In this work, a single-beam lidar is simulated in the spinner of a bottom-fixed IEA 15 MW wind turbine. Both continuous-wave (CW) and pulsed lidar systems are considered. The single-beam lidar can rotate with the wind turbine rotor and scan the inflow with a circular pattern, which mimics a multiple-beam nacelle lidar at a lower cost. Also, the spinner-based lidar has an unimpeded view of the inflow without intermittent blockage from the rotating blade. The focus distance and the cone angle of the spinner-based single-beam lidar are optimized for the best wind preview quality based on a rotor-effective wind speed coherence model. Then, the control benefits of using the optimized spinner-based lidar are evaluated for an above-rated wind speed in OpenFAST with an embedded lidar simulator and virtual four-dimensional Mann turbulence fields considering the wind evolution. Results are compared against those using a single-beam nacelle-based lidar. We found that the optimum scanning configurations of both CW and pulsed spinner-based single-beam lidars lead to a lidar scan radius of 0.6 of the rotor radius. Also, results show that a single-beam lidar mounted in the spinner provides many more control benefits (i.e. better rotor speed regulations and higher reductions in the damage equivalent loads on the tower base and blade roots) than the one based on the nacelle. The spinner-based single-beam lidar has a similar performance to a four-beam nacelle lidar when used for feedforward control.

Lidar-assisted control (LAC) of wind turbines is a control concept that takes advantage of a nacelle-mounted lidar (a remote sensing device) to measure upstream wind speeds of a turbine to allow a preview of the incoming turbulence. Because the turbine will not be exposed to the identical turbulence as that measured by the lidar in advance, the simulation of a LAC system will be more realistic if wind evolution can be modeled in the wind field generation. Since the commonly used 3D stochastic wind field generation method does not include wind evolution, the main goal of this research is to extend the 3D method to 4D to enable the modeling of wind evolution along the wind direction. The most novel part of this research is that we propose a two-step Cholesky decomposition approach for the factorization of the coherence matrices in the wind field generation. With this approach, 4D wind fields can be generated by combining multiple statistically independent 3D wind fields. To enable better integration of the 4D method into the common workflow of wind turbine simulations, we implement the 4D method as the open-access tool evoTurb in combination with TurbSim and Mann turbulence generator. Moreover, since 4D wind field generation is supposed to be coupled with lidar simulations, and considering the range weighting effect of lidars and eventually multiple range gates, a 4D wind field will contain many more simulation points than a 3D one. To avoid excessive computational effort, we further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide some insights to optimize the application of 4D wind field generation.