Eric Simley

Model Predictive Control Using Preview Measurements From LIDAR y

Light detection and ranging (LIDAR) systems are able to measure conditions at a distance in front of wind turbines and are therefore suited to providing preview information of wind disturbances before they impact the turbine blades. In this study, a time-varying model predictive controller is developed that uses preview measurements of wind speeds approaching the turbine. Performance of the controller is evaluated using ideal, undistorted measurements at positions that rotate with the turbine blade and measurements obtained at the same locations, but including distortion characteristic of LIDAR systems. Using these measurements, the model predictive controller is simulated in turbulent wind conditions and its performance is compared against previously designed, linear-time-invariant H1 preview controllers and industry standard controllers. Surprisingly, even though the LIDAR distortions produce signicant measurement error, controller performance is found to surpass that obtained using individual-pitch feedback-only controllers without preview. In previous studies, errors introduced articially, but of the same order of magnitude, were shown to degrade the performance of preview control so that it is worse than using feedback only. In this study, we also incorporate a simple error model to compensate the eect of LIDAR induced error, but nd that it does not improve performance.

Adding Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter Methods

Combined feedback/feedforward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent winds. The feedforward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Three feedforward designs are studied: Non-Causal Series Expansion, Preview Control, and Optimized FIR Filter. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by lidar. Noncausal series expansion and Preview Control methods reduce blade root loads but increase tower bending in simulation results. The optimized FIR filter reduces loads overall, keeps pitch rates low, and maintains rotor speed regulation and power capture, while using imperfect wind measurements provided by a lidar model.

LIDAR Wind Speed Measurements of Evolving Wind Fields

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylors frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylors hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements are also evaluated with a large eddy simulation of a stable boundary layer provided by the National Center for Atmospheric Research. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.

Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars

As a wind turbine generates power, induced velocities, lower than the freestream velocity, will be present upstream of the turbine due to perturbation of the flow by the rotor. In this study, the upstream induction zone of a 225 kW horizontal axis Vestas V27 wind turbine located at the Danish Technical Universitys Riso campus is investigated using a scanning Light Detection and Ranging (lidar) system. Three short-range continuous-wave “WindScanner” lidars are positioned in the field around the V27 turbine allowing detection of all three components of the wind velocity vectors within the induction zone. The time-averaged mean wind speeds at different locations in the upstream induction zone are measured by scanning a horizontal plane at hub height and a vertical plane centered at the middle of the rotor extending roughly 1.5 rotor diameters (D) upstream of the rotor. Turbulence statistics in the induction zone are studied by more rapidly scanning along individual lines perpendicular to the rotor at differ...

Investigation of the Impact of the Upstream Induction Zone on LIDAR Measurement Accuracy for Wind Turbine Control Applications using Large-Eddy Simulation

Several sources of error exist in lidar measurements for feedforward control of wind turbines including the ability to detect only radial velocities, spatial averaging, and wind evolution. This paper investigates another potential source of error: the upstream induction zone. The induction zone can directly affect lidar measurements and presents an opportunity for further decorrelation between upstream wind and the wind that interacts with the rotor. The impact of the induction zone is investigated using the combined CFD and aeroelastic code SOWFA. Lidar measurements are simulated upstream of a 5 MW turbine rotor and the true wind disturbances are found using a wind speed estimator and turbine outputs. Lidar performance in the absence of an induction zone is determined by simulating lidar measurements and the turbine response using the aeroelastic code FAST with wind inputs taken far upstream of the original turbine location in the SOWFA wind field. Results indicate that while measurement quality strongly depends on the amount of wind evolution, the induction zone has little effect. However, the optimal lidar preview distance and circular scan radius change slightly due to the presence of the induction zone.

Reducing LIDAR wind speed measurement error with optimal filtering

Recent research has shown the potential for reduction in wind turbine generator speed error and structural loads with the introduction of feedforward control using preview LIDAR measurements. Several sources of error exist in the estimation of the wind speeds that will interact with the turbine rotor, including LIDAR distortion and coherence loss due to wind evolution. If a feedforward controller is designed assuming perfect wind speed measurements, however, the error in the disturbance estimate may cause feedforward control to increase output errors. Here we derive the minimum mean square error feedforward controller for imperfect measurements using statistical descriptions of the wind. We show that the resulting controller is the ideal feedforward controller, assuming perfect measurements, in series with a Wiener prefilter to reduce the mean square error of the disturbance estimate. We derive the optimal filter in the frequency domain assuming infinite preview as well as the optimal filter in the time domain with preview time constraints. Examples illustrating the error reduction with optimal prefiltering are provided for simulated control and measurement scenarios.

A spectral model for evaluating the effect of wind evolution on wind turbine preview control

As wind turbines become larger and more flexible, the potential benefits of load mitigating control systems become more important to reduce fatigue and extend component life. In the last five years, there has been significant research activity exploring the effectiveness of preview control techniques that may be feasible using advanced wind measurement technologies like LIDAR (light detection and ranging). However, most control development tools use Taylors frozen turbulence hypothesis. The end result is that preview measurements made up-stream from the rotor can be obtained with unrealistic accuracy, because the same wind velocities eventually arrive at the turbine. In this study, we extend the spectral methods commonly used to generate turbulent wind fields for controls simulation, but in a way that emulates wind evolution. This changes preview measurements made upwind from the rotor, in such a way that the differences- between the preview measurements and speeds arriving at the turbine- increase with distance from the rotor. We then evaluate the degradation in load mitigation performance of a controller that uses preview measurements obtained at various distances in front of the rotor.

Correlation between Rotating LIDAR Measurements and Blade Effective Wind Speed

Preview wind speed measurements from a forward looking Light Detection and Ranging (LIDAR) system located in the hub of a wind turbine can be used by a feedforward blade pitch control system to mitigate structural loads. For individual blade pitch control, a separate preview estimate of the e ective wind speed encountered by each blade must be available. One way of providing an estimate of the blade e ective wind speed is to implement a hub mounted spinning lidar that scans the wind eld at the rotational rate of the rotor such that the measured wind will reach the blade after some delay. In this way, both the lidar measurement and the wind turbine blade rotationally sample the wind eld. The bene t gained by using preview wind speed measurements strongly depends on the correlation between the measured wind and the wind that interacts with the blades. In this research, the coherence between rotating lidar measurements and blade e ective wind speed is calculated and analyzed using a spectral model of the wind eld. The simulated wind eld uses an isotropic von Karman spectrum and contains a model of wind evolution described by a longitudinal spatial coherence function. The coherence between stationary measurements and stationary blade e ective wind speeds decreases to zero near the 1P rotational frequency of the turbine. However, measurement coherence between rotating lidar measurements and blade e ective wind speed remains much higher and does not decay until higher frequencies.

A longitudinal spatial coherence model for wind evolution based on large-eddy simulation

Standard feedback controllers on wind turbines can be augmented with feedforward control, relying on preview measurements of the wind provided by remote sensing instruments, to help regulate rotor speed and reduce structural loads. The effectiveness of feedforward control depends on how accurately the approaching wind can be measured. One significant cause of measurement error is the evolution of the wind as it travels toward the turbine from the measurement location. Wind evolution is commonly quantified using longitudinal spatial coherence to describe the decorrelation of turbulence as the wind advects downstream. In this paper, a collection of wind fields produced by large-eddy simulation is used to calculate longitudinal coherence for a variety of atmospheric conditions. Using the calculated coherence curves, we determine a simple longitudinal coherence formula for approximating wind evolution, which depends on mean wind speed, turbulent kinetic energy, and turbulence length scale. This formula is then used to find the optimal scan configurations that minimize measurement error for a preview-based control scenario employing Light Detection and Ranging. Results show how the optimal preview distance and achievable measurement error depend on the aforementioned wind parameters.

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.