Florian Haizmann

Field Testing LIDAR Based Feed-Forward Controls on the NREL Controls Advanced Research Turbine

Wind turbines are complex, nonlinear, dynamic systems driven by aerodynamic, gravitational, centrifugal, and gyroscopic forces. The aerodynamics of wind turbines are nonlinear, unsteady, and complex. Turbine rotors are subjected to a chaotic three-dimensional (3-D) turbulent wind inflow field with imbedded coherent vortices that drive fatigue loads and reduce lifetime. In order to reduce cost of energy, future large multimegawatt turbines must be designed with lighter weight structures, using active controls to mitigate fatigue loads, maximize energy capture, and add active damping to maintain stability for these dynamically active structures operating in a complex environment. Researchers at the National Renewable Energy Laboratory (NREL) and University of Stuttgart are designing, implementing, and testing advanced feed-back and feed-forward controls in order to reduce the cost of energy for wind turbines.

Field testing of feedforward collective pitch control on the CART2 using a nacelle-based Lidar scanner

This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.

Three Dimensional Dynamic Model Based Wind Field Reconstruction from Lidar Data

Using the inflowing horizontal and vertical wind shears for individual pitch controller is a promising method if blade bending measurements are not available. Due to the limited information provided by a lidar system the reconstruction of shears in real-time is a challenging task especially for the horizontal shear in the presence of changing wind direction. The internal model principle has shown to be a promising approach to estimate the shears and directions in 10 minutes averages with real measurement data. The static model based wind vector field reconstruction is extended in this work taking into account a dynamic reconstruction model based on Taylors Frozen Turbulence Hypothesis. The presented method provides time series over several seconds of the wind speed, shears and direction, which can be directly used in advanced optimal preview control. Therefore, this work is an important step towards the application of preview individual blade pitch control under realistic wind conditions. The method is tested using a turbulent wind field and a detailed lidar simulator. For the simulation, the turbulent wind field structure is flowing towards the lidar system and is continuously misaligned with respect to the horizontal axis of the wind turbine. Taylors Frozen Turbulence Hypothesis is taken into account to model the wind evolution. For the reconstruction, the structure is discretized into several stages where each stage is reduced to an effective wind speed, superposed with a linear horizontal and vertical wind shear. Previous lidar measurements are shifted using again Taylors Hypothesis. The wind field reconstruction problem is then formulated as a nonlinear optimization problem, which minimizes the residual between the assumed wind model and the lidar measurements to obtain the misalignment angle and the effective wind speed and the wind shears for each stage. This method shows good results in reconstructing the wind characteristics of a three dimensional turbulent wind field in real-time, scanned by a lidar system with an optimized trajectory.

Full-Scale Field Test of Wake Steering

Wind farm control, in which turbine controllers are coordinated to improve farmwide performance, is an active field of research. One form of wind farm control is wake steering, in which a turbine is yawed to the inflow to redirect its wake away from downstream turbines. Wake steering has been studied in depth in simulations as well as in wind tunnels and scaled test facilities. This work performs a field test of wake steering on a full-scale turbine. In the campaign, the yaw controller of the turbine has been set to track different yaw misalignment set points while a nacelle-mounted lidar scans the wake at several ranges downwind. The lidar measurements are combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast. These measurements are then compared to the predictions of a wind farm control-oriented model of wakes.

Direct Speed Control using LIDAR and turbine data

LIDAR systems are able to provide preview information of the wind speed in front of wind turbines. One proposed use of this information is to increase the energy capture of the turbine by adjusting the rotor speed directly to maintain operation at the optimal tip-speed ratio, a technique referred to as Direct Speed Control (DSC). Previous work has indicated that for large turbines the marginal benefit of the direct speed controller in terms of increased power does not compensate for the increase of the shaft loads. However, the technique has not yet been adequately tested to make this determination conclusively. Further, it is possible that applying DSC to smaller turbines could be worthwhile because of the higher rotor speed fluctuations and the small rotor inertia. This paper extends the previous work on direct speed controllers. A DSC is developed for a 600 kW experimental turbine and is evaluated theoretically and in simulation. Because the actual turbine has a mounted LIDAR, data collected from the turbine and LIDAR during operation are used to perform a hybrid simulation. This technique allows a realistic simulation to be performed, which provides good agreement with theoretical predictions.

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 lidars capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbines reaction was confirmed from the measured data. Additionally, the baseline feedback controllers 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.

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop “Optimizing Lidar Design for Wind Energy Applications” was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.

Systems Engineering for Lidar-Assisted Control: A Sequential Approach

This work presents a sequential approach to explore and optimize the benefits of lidar-assisted control for wind turbines. The optimization is divided in three steps: lidar hardware, lidar data processing, and feedback controller optimization. Appropriate optimization criteria and computational efficient models are used for the intermediate steps and energy production is optimized in the last step with a full aero-elastic model to provide an estimation of lidar-assisted control without the need of a detailed cost model. The case study shows that lidar-assisted control together with an adjustment of the power level are promising to extend the life-time of wind turbines and finally increase the energy capture.

Lidar-assisted Extreme Load Reduction by Multi-variable Protective Derating

Lidar-assisted control of wind turbines has been an active field of research during the last years and has recently become more attention from industry, as well. The potential of lidar-assisted feed-forward controllers is shown in various simulation studies and proven in first field-testings. This work aims to further push forward the application of lidar-assisted control by introducing a method to bring the wind turbine to a different operating point, when a lidar detects an approaching extreme gust. The method reduces the power output of the wind turbine while keeping the rotor rotation constant. This is achieved by a synchronous multi-variable feed-forwarding of the generator torque and the pitch angle. In combination with a classical lidar-assisted feed-forward controller, this leads to further reduced structural loads under the impact of an extreme gust. The method is tested with a coherent and a corresponding turbulent gust.

Comparison of different measurement methods for a nacelle-based lidar power curve

In this study, different methods are used to determine a nacelle-based lidar power curve. The methods used have already been published in other studies. However, these studies differed in environmental conditions, rated power, hub height, rotor diameter and in the evaluation criteria of the power curve. In this study, the published methods for the determination of nacelle-based lidar power curves will be used and evaluated with uniform criteria. The basis for this is provided by measurements of ground- and nacelle-based lidar and met mast measurements at an IEC-conform site. The results show that some methods significantly reduce scattering in the performance curve and that the choice of measurement distances used has a significant influence on the quality of the results.