ALBERT

Description: Horizontal axis wind turbines routinely suffer significant time varying aerodynamic loads that adversely impact structures, mechanical components, and power production. As lighter and more flexible wind turbines are designed to reduce overall cost of energy, greater accuracy and reliability will become even more crucial in future aerodynamics models. However, to render calculations tractable, current modeling approaches admit various approximations that can degrade model predictive accuracy. To help understand the impact of these modeling approximations and improve future models, the current effort seeks to document and comprehend the vortex kinematics for three-dimensional, unsteady, vortex dominated flows occurring on horizontal axis wind turbine blades during non-zero yaw conditions. To experimentally characterize these flows, the National Renewable Energy Laboratory Unsteady Aerodynamics Experiment turbine was erected in the NASA Ames 80 ft×120 ft wind tunnel. Then, under strictly-controlled inflow conditions, turbine blade surface pressures and local inflow velocities were acquired at multiple radial locations. Surface pressure histories and normal force records were used to characterize dynamic stall vortex kinematics and normal forces. Stall vortices occupied approximately two-thirds of the aerodynamically active blade span and persisted for nearly one-fourth of the blade rotation cycle. Stall vortex convection varied dramatically along the blade radius, yielding pronounced dynamic stall vortex deformation. Analysis of these data revealed systematic alterations to vortex kinematics due to changes in test section speed, yaw error, and blade span location.

Description: Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number (RB). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different RB regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to incorporate effects of atmospheric stability and turbulence across the rotor disk.

Description: Despite their potential as a valuable source of individual turbine power performance and turbine array energy production optimization information, nacelle-mounted anemometers have often been neglected because complex flows around the blades and nacelle interfere with their measurements. This work quantitatively explores the accuracy of and potential corrections to nacelle anemometer measurements to determine the degree to which they may be useful when corrected for these complex flows, particularly for calculating annual energy production (AEP) in the absence of other meteorological data. Using upwind meteorological tower measurements along with nacelle-based measurements from a General Electric (GE) 1.5sle model, we calculate empirical nacelle transfer functions (NTFs) and explore how they are impacted by different atmospheric and turbulence parameters. This work provides guidelines for the use of NTFs for deriving useful wind measurements from nacelle-mounted anemometers. Corrections to the nacelle anemometer wind speed measurements can be made with NTFs and used to calculate an AEP that comes within 1 % of an AEP calculated with upwind measurements. We also calculate unique NTFs for different atmospheric conditions defined by temperature stratification as well as turbulence intensity, turbulence kinetic energy, and wind shear. During periods of low stability as defined by the Bulk Richardson number (RB), the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of high stability at some wind speed bins below rated speed, leading to a steeper NTF during periods of low stability. Similarly, during periods of high turbulence, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of low turbulence at most wind bins between cut-in and rated wind speed. Based on these results, we suggest different NTFs be calculated for different regimes of atmospheric stability and turbulence for power performance validation purposes.

Description: The problem under consideration in this viewgraph presentation is to understand, predict, and control the fluid mechanics of dynamic maneuvers, unsteady boundary layers, and vortex dominated flows. One solution is the application of neural networks demonstrating closed-loop control. Neural networks offer unique opportunities: simplify modeling of three dimensional, vortex dominated, unsteady separated flow fields; are effective means for controlling unsteady aerodynamics; and address integration of sensors, controllers, and time lags into adaptive control systems.