Lidar-based wake tracking for closed-loop wind farm control

This work presents two advancements towards closed-loop wake redirection of a wind turbine. First, a model-based wake-tracking approach is presented, which uses a nacelle-based lidar system facing downwind to obtain information about the wake. The method uses a reduced-order wake model to track the wake. The wake tracking is demonstrated with lidar measurement data from an offshore campaign and with simulated lidar data from a simulation with the Simulator fOr Wind Farm Applications (SOWFA). Second, a controller for closed-loop wake steering is presented. It uses the wake-tracking information to set the yaw actuator of the wind turbine to redirect the wake to a desired position. Altogether, the two approaches enable a closed-loop wake redirection

Prospects of linear model predictive control on a 10MW floating wind turbine

The presented research has the objective of supporting the integrated conceptual design of floating offshore wind turbines (FOWT). The dynamics of the multidisciplinary coupled system with the aerodynamics, hydrodynamics, structural dynamics, the catenary mooring lines and the controller shall be represented in simulation models adapted to the current design stage. Here, a linear model-predictive controller (MPC) as an optimal multiple input-multiple output (MIMO) controller is designed for a novel concept of the floating foundation for a 10MW wind turbine. The performance of this controller is easily adjustable by a cost function with multiple objectives. Therefore, the MPC can be seen as a benchmark controller in the concept phase, based on a simplified coupled simulation model with only approximate model information. The linear model is verified against its nonlinear counterpart and the performance of the MPC compared to a SISO PI-controller, which is also designed in this work. The developed models show to be well suited and the linear MPC shows a reduction of the rotor speed overshoot and tower bending from a deterministic gust

Wake redirecting using feedback control to improve the power output of wind farms

In future, a wind turbine will not only be seen as a single systems operating independently, but also as a component of a larger interacting system, the wind farm. To increase the efficiency of a wind farm, two main concepts have been proposed: axial induction control and wake redirecting. This contribution focuses on the latter. Remote sensing technologies in wind energy applications have opened new ways to control wind turbines. In this contribution, a further step is taken by using a remote sensing device for redirecting the wake of a wind turbine. A controller is proposed which uses the yaw actuator of a wind turbine to steer the wake center of the turbine to a desired position. The wake propagation from the wind turbine to the measurement location is modeled with a time delay. This forms a challenging problem for controller design. The controller follows the idea of the internal model principle and uses a model to predict the system behavior avoiding an overestimation of the error. Further, an adaptive filter is proposed in order to filter uncontrollable frequencies from the wake center estimation. The estimation from lidar measurement data is assumed to be perfect. Closed-loop simulations are conducted using the nominal system and a wind farm simulation tool, which was adapted to the scenario. The results are compared to the uncontrolled baseline case and a statically applied yaw offset. They show an increase in the total power output of the wind farm. Together with wake tracking methods, the approach can be considered as a promising step towards closed-loop wind farm control

Shadow effects in an offshore wind farm - potential of vortex methods for wake modelling

Offshore wind turbines in a wind farm are affected by wakes of upstream turbines and adjacent wind farms depending on the park layout and wind direction. As a result the power output may decrease, while structural loads are increasing. In this research a coupled numerical approach based on multi-body system and free vortex methods is used to simulate shadow effects on the Alpha Ventus wind farm. The AV5 is operating at 12 m/s wind speed at half wake conditions with enabled control system and flexible blades and tower. Results of power output, rotor speed, blade pitch and blade root moment over time and azimuth demonstrate the high impact of the half wake condition on the wind turbine performance and loads

Nonlinear model predictive control of floating wind turbines with individual pitch control

In this work a nonlinear model predictive controller with individual pitch control for a floating offshore wind turbine is presented. An aerodynamic model of the collective pitch control approach is extended by describing pitching and yawing moments based on rotor disk theory. This extension is implemented in a reduced nonlinear model of the floating wind turbine including disturbance preview of wind speed, linear vertical and horizontal wind shear, and wave height to compute optimal input trajectories for the individual pitch control inputs and the generator torque. An extended cost functional for individual pitch control is proposed based on the collective pitch control approach. The controller is evaluated in aero-servo-hydro-elastic simulations of a 5MW reference wind turbine disturbed by a three-dimensional stochastic turbulent wind field. Results show a significant blade fatigue load reduction compared to a baseline controller through minimizing yawing and pitching moments on the rotor hub while maintaining the advantages of the model predictive control approach with collective pitch control

Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed

An adaptive data processing technique for lidar-assisted control to bridge the gap between lidar systems and wind turbines

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control

Optimization of a feed-forward controller using a CW-lidar system on the CART3

This work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar's capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine's reaction was confirmed from the measured data. Additionally, the baseline feedback controller's gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper

Multi-variable feedforward control for floating wind turbines using lidar

In this work a multi-variable feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate and a torque update to a conventional feedback controller based on a wind speed preview. A 10 MW reference wind turbine is used on a semi submersible floating platform to study the potential of the controller. An open-source simulation tool is extended with an realistic lidar simulator and the lidar data processing, feedforward controller, and feedback controller are implemented in modular setup. The lidar measurements are fully motion compensated and combined to provide a preview of the rotor-effective wind speed to the controller. The feedforward controller is designed to minimize structural loads and to decrease the platform pitch motion. In verification and simulation studies the concept is demonstrated and the multi-variable feedforward controller shows a promising improvement in speed regulation and load reduction on the floating wind turbine

A Tutorial on Lidar-Assisted Control for Floating Offshore Wind Turbines

<p>Presentation at the American Control Conference 2023, San Diego, CA, USA.</p><p>Paper at <a href="https://doi.org/10.23919/ACC55779.2023.10156419">https://doi.org/10.23919/ACC55779.2023.10156419 .</a></p&gt