Neil Kelley

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.

Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake

AbstractAn experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).

Combining Standard Feedback Controllers with Feedforward Blade Pitch Control for Load Mitigation in Wind Turbines

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. Various collective pitch and individual pitch versions of two feedforward designs are studied: Gain-Scheduled Model-Inverse and Gain-Scheduled Shaped Compensator. The input to the feedforward controller is a measurement of incoming wind speed, which could potentially be provided by LIDAR. Three of the designs reduce structural loading compared to standard feedback control, without reducing power production.

Horizontal Velocity and Variance Measurements in the Stable Boundary Layer Using Doppler Lidar: Sensitivity to Averaging Procedures

Abstract Quantitative data on turbulence variables aloft—above the region of the atmosphere conveniently measured from towers—have been an important but difficult measurement need for advancing understanding and modeling of the stable boundary layer (SBL). Vertical profiles of streamwise velocity variances obtained from NOAA’s high-resolution Doppler lidar (HRDL), which have been shown to be approximately equal to turbulence kinetic energy (TKE) for stable conditions, are a measure of the turbulence in the SBL. In the present study, the mean horizontal wind component U and variance σ2u were computed from HRDL measurements of the line-of-sight (LOS) velocity using a method described by Banta et al., which uses an elevation (vertical slice) scanning technique. The method was tested on datasets obtained during the Lamar Low-Level Jet Project (LLLJP) carried out in early September 2003, near the town of Lamar in southeastern Colorado. This paper compares U with mean wind speed obtained from sodar and sonic an...

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.

The NREL Large-Scale Turbine Inflow and Response Experiment ― Preliminary Results

The accurate numerical dynamic simulation of new large-scale wind turbine designs operating over a wide range of inflow environments is critical because it is usually impractical to test prototypes in a variety of locations. Large turbines operate in a region of the atmospheric boundary layer that currently may not be adequately simulated by present turbulence codes. In this paper, we discuss the development and use of a 42-m (137-ft) planar array of five, high-resolution sonic anemometers upwind of a 600-kW wind turbine at the National Wind Technology Center (NWTC). The objective of this experiment is to obtain simultaneously collected turbulence information from the inflow array and the corresponding structural response of the turbine. The turbulence information will be used for comparison with that predicted by currently available codes and establish any systematic differences. These results will be used to improve the performance of the turbulence simulations. The sensitivities of key elements of the turbine aeroelastic and structural response to a range of turbulence-sc aling parameters will be established for comparisons with other turbines and operating environments. In this paper, we present an overview of the experiment, and offer examples of two observed cases of inflow characteristics and turbine response collected under daytime and nighttime conditions, and compare their turbulence properties with predictions. *

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.

Measurements of Wind and Turbulence Profiles With Scanning Doppler Lidar for Wind Energy Applications

High-quality profiles of mean and turbulent statistics of the wind field upstream of a wind farm can be produced using a scanning Doppler lidar. Careful corrections for the spatial filtering of the wind field by the lidar pulse produce turbulence estimates equivalent to point sensors but with the added advantage of a larger sampling volume to increase the statistical accuracy of the estimates. For a well-designed lidar system, this permits accurate estimates of the key turbulent statistics over various subdomains and with sufficiently short observation times to monitor rapid changes in conditions. These features may be ideally suited for optimal operation of wind farms and also for improved resource assessment of potential sites.

Turbine Inflow Characterization at the National Wind Technology Center

Utility-scale wind turbines operate in dynamic flows that can vary significantly over timescales from less than a second to several years. To better understand the inflow to utility-scale turbines, two inflow towers were installed and commissioned at the National Renewable Energy Laboratorys (NREL) National Wind Technology Center near Boulder, Colorado, in 2011. These towers are 135 m tall and instrumented with a combination of sonic anemometers, cup anemometers, wind vanes, and temperature measurements to characterize the inflow wind speed and direction, turbulence, stability and thermal stratification to two utility-scale turbines. Herein, we present variations in mean and turbulent wind parameters with height, atmospheric stability, and as a function of wind direction that could be important for turbine operation as well as persistence of turbine wakes. Wind speed, turbulence intensity, and dissipation are all factors that affect turbine performance. Our results show that these all vary with height across the rotor disk, demonstrating the importance of measuring atmospheric conditions that influence wind turbine performance at multiple heights in the rotor disk, rather than relying on extrapolation from lower levels.

3D Volumetric Analysis of Wind Turbine Wake Properties in the Atmosphere Using High-Resolution Doppler Lidar

AbstractWind turbine wakes in the atmosphere are three-dimensional (3D) and time dependent. An important question is how best to measure atmospheric wake properties, both for characterizing these properties observationally and for verification of numerical, conceptual, and physical (e.g., wind tunnel) models of wakes. Here a scanning, pulsed, coherent Doppler lidar is used to sample a turbine wake using 3D volume scan patterns that envelop the wake and simultaneously measure the inflow profile. The volume data are analyzed for quantities of interest, such as peak velocity deficit, downwind variability of the deficit, and downwind extent of the wake, in a manner that preserves the measured data. For the case study presented here, in which the wake was well defined in the lidar data, peak deficits of up to 80% were measured 0.6–2 rotor diameters (D) downwind of the turbine, and the wakes extended more than 11D downwind. Temporal wake variability over periods of minutes and the effects of atmospheric gusts a...