Engineering topics
Kelley, Neil
Publications and source records attributed to Kelley, Neil.
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 Laboratory's (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.
Characterizing Inflow Conditions Across the Rotor Disk of a Utility-Scale Wind Turbine (Poster)
Multi-megawatt utility-scale wind turbines operate in a turbulent, thermally-driven atmosphere where wind speed and air temperature vary with height. Turbines convert the wind's momentum into electrical power, and so changes in the atmosphere across the rotor disk influence the power produced by the turbine. To characterize the inflow into utility scale turbines at the National Wind TechnologyCenter (NWTC) near Boulder, Colorado, NREL recently built two 135-meter inflow monitoring towers. This poster introduces the towers and the measurements that are made, showing some of the data obtained in the first few months of operation in 2011.
Comparison of Two Independent Lidar-Based Pitch Control Designs
Two different lidar-based feedforward controllers have previously been designed for the NREL 5 MW wind turbine model under separate studies. One uses a finite-impulse-response design, with 5 seconds of preview, and three rotating lidar measurements. The other uses a static-gain design, with the preview time defined by the pitch actuator dynamics, a simulation of a real nacelle-based scanning lidar system, and a lowpass filter defined by the lidar configuration. These controllers are now directly compared under the same lidar configuration, in terms of fatigue load reduction, rotor speed regulation, and power capture. The various differences in design choices are discussed and compared. We also compare frequency plots of individual pitch feedforward and collective pitch feedforward load reductions, and we see that individual pitch feedforward is effective mainly at the once-per-revolution and twice-per-revolution frequencies. We also explain how to determine the required preview time by breaking it down into separate parts, and we then compare it to the expected preview time available.
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 that are 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 the validity of physicist G.I. Taylor's 1938 frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor's 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 using the National Renewable Energy Laboratory's (NREL's) 5-megawatt turbine model to create a more realistic measurement model. A simple model of wind evolution was applied to a frozen wind field that was used in previous studies to investigate the effects of varying the intensity of wind evolution. LIDAR measurements were also evaluated using a large eddy simulation (LES) of a stable boundary layer that was provided by the National Center for Atmospheric Research. The LIDAR measurement scenario investigated consists of a hub-mounted LIDAR that scans a circle of points upwind of the turbine in order to estimate the wind speed component in the mean wind direction. Different combinations of the preview distance that is located upwind of the rotor and the radius of the scan circle were analyzed. It was found that the dominant source of measurement error for short preview distances is the detection of transverse and vertical wind speeds from the line-of-sight LIDAR measurement. It was discovered in previous studies that, in the absence of wind evolution, the dominant source of error for large preview distances is the spatial averaging caused by the LIDAR's sampling volume. However, by introducing wind evolution, the dominant source of error for large preview distances was found to be the coherence loss caused by evolving turbulence. Different measurement geometries were compared using the bandwidth for which the measurement coherence remained above 0.5 and also the area under the measurement coherence curve. Results showed that, by increasing the intensity of wind evolution, the measurement coherence decreases. Using the coherence bandwidth metric, the optimal preview distance for a fixed-scan radius remained almost constant for low and moderate amounts of wind evolution. For the wind field with the simple wind evolution model introduced, the optimal preview distance for a scan radius of 75% blade span (47.25 meters) was found to be 80 meters. Using the LES wind field, the optimal preview distance was 65 meters. When comparing scan geometries using the area under the coherence curve, results showed that, as the intensity of wind evolution increases, the optimal preview distance decreases.