Andrew Scholbrock

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.

Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment

In this paper, a nacelle-mounted lidar was used to improve the yaw alignment of an experimental wind turbine. Using lidar-recorded data during normal operation, an error correction value for the nacelle vane wind direction measurement used in the yaw controller was determined. A field test was then conducted in which the turbine was operated with and without the correction applied to the yaw controller. Results demonstrated a significant increase in power capture. In addition, the study includes analysis on the impacts on loading of applying this yaw correction. The study demonstrates a successful application in field testing of using a nacelle-mounted lidar to improve turbine performance.

A tutorial on control-oriented modeling and control of wind farms

Wind turbines are often sited together in wind farms as it is economically advantageous. However, the wake inevitably created by every turbine will lead to a time-varying interaction between the individual turbines. Common practice in industry has been to control turbines individually and ignore this interaction while optimizing the power and loads of the individual turbines. However, turbines that are in a wake experience reduced wind speed and increased turbulence, leading to a reduced energy extraction and increased dynamic mechanical loads on the turbine, respectively. Neglecting the dynamic interaction between turbines in control will therefore lead to suboptimal behaviour of the total wind farm. Therefore, wind farm control has been receiving an increasing amount of attention over the past years, with the focus on increasing the total power production and reducing the dynamic loading on the turbines. In this paper, wind farm control-oriented modeling and control concepts are explained. In addition, recent developments and literature are discussed and categorized. This paper can serve as a source of background information and provides many references regarding control-oriented modeling and control of wind farms.

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.

Detailed field test of yaw-based wake steering

This paper describes a detailed field-test campaign to investigate yaw-based wake steering. In yaw-based wake steering, an upstream turbine intentionally misaligns its yaw with respect to the inflow to deflect its wake away from a downstream turbine, with the goal of increasing total power production. In the first phase, a nacelle-mounted scanning lidar was used to verify wake deflection of a misaligned turbine and calibrate wake deflection models. In the second phase, these models were used within a yaw controller to achieve a desired wake deflection. This paper details the experimental design and setup. All data collected as part of this field experiment will be archived and made available to the public via the U.S. Department of Energys Atmosphere to Electrons Data Archive and Portal.

Using particle filters to track wind turbine wakes for improved wind plant controls

Recently there has been interest in the design of wind farm control systems that can coordinate individual turbine controllers to improve global plant performance. This improvement comes from accounting for the way in which turbines interact through wakes. Often however, controllers are designed assuming steady and known environmental conditions, without turbulence or wake meandering. This raises the concern that these methods will fail to perform well in practice because it could be difficult to apply methods based on steady wakes to a situation where wake locations are changing and not measurable. In this paper, a particle filter is used to continually estimate the wake locations in a stochastic setting by combining all of the available turbine measurements. The design of the algorithm is documented, and is shown to employ sensors that are available on modern turbines. Using a high-fidelity wind farm simulator, we show the effectiveness of the proposed framework using several multi-turbine scenarios and compare the wake locations predicted against the wakes observable in flow-field slices taken from the simulator output.

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.

Field testing a wind turbine drivetrain/tower damper using advanced design and validation techniques

As an ongoing trend, the design of wind turbines is moving towards larger machines with components optimized for cost effectiveness. This leads to very large and flexible structures with lightly damped modes. Good control design is becoming more essential to ensure safe and stable operation over the life of the turbine. Additionally, there is growing interest in expanding the number of sensors and actuators available for closed-loop control. The increasing number of control variables in modern wind turbines will necessitate model-based controller design to handle the complexity of the flexible and coupled control loops in an effective and robust way. Recent literature in wind energy has explored the use of modern control design techniques such as state-space and robust control design methods and closed-loop system identification for model identification. However, this literature is, to date, mostly confined to simulation studies. Field testing is necessary to demonstrate the effectiveness of advanced design and validation techniques in a practical application. In this paper, we design two alternative dampers for the lightly damped drivetrain and tower modes of an experimental turbine. One design is based on classical iterative design approaches, while the other uses an H∞ approach. The two controllers are then validated using two alternative methods: the traditional extended field test and a relatively short system identification experiment. The paper demonstrates that even in this sub-problem of wind turbine control consisting of only two loops, the use of advanced design and validation techniques is very effective at converging quickly to good control designs and quickly assessing their performance.