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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The effect of wind direction shear on turbine performance in a wind farm in central Iowa

Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance; however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, and speed shear affect wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Wind direction and speed shear were found to follow a diurnal cycle; however, they evolved differently with increasing wind speeds. Using a combination of speed and direction shear values, we found large direction and small speed shear to result in underperformance. We further analyzed the effects of wind veering on turbine performance for specific values of speed shear and found detrimental conditions on the order of 10 % for wind speed regimes predominantly located in the middle of the power curve. Focusing on a time period of ramping electricity demand (06:00–09:00 LT – local time) exposed the fact that large direction shear occurred during this time and undermined turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance.

17 WIND ENERGY↗
DOE OSTI2013

Using Machine Learning to Create Turbine Performance Models (Presentation)

Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.

17 WIND ENERGY↗
DOE OSTI2019

The Effect of Wind Direction Shear on Turbine Performance in a Wind Farm in Central Iowa

Numerous studies have shown that atmospheric conditions affect wind turbine performance, however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, affects wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Directional wind shear was found to follow a diurnal cycle and to monotonically decrease with increasing wind speeds. Using different thresholds to distinguish between high- and low-directional wind shear scenarios, we found that larger thresholds evidence statistically-significant effects on turbine power production for lower wind speeds. We further analyzed a threshold of 0.225 deg m-1 and found turbine underperformance in the order of 10% for wind speed regimes below 8 m s-1. Considering a time period of ramping electricity demand (05:30-09:00LT) exposed the fact that large direction shear occurs during this time and is undermining turbine performance by more than 10%. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance, however, future work on segregating speed and direction shear should be pursued to quantify the effects of only one factor on turbine power production.

17 WIND ENERGY↗
DOE OSTI2012

Effects of Changing Atmospheric Conditions on Wind Turbine Performance (Poster)

Multi-megawatt, utility-scale wind turbines operate in turbulent and dynamic winds that impact turbine performance in ways that are gradually becoming better understood. This poster presents a study made using a turbulent flow field simulator (TurbSim) and a Turbine aeroelastic simulator (FAST) of the response of a generic 1.5 MW wind turbine to changing inflow. The turbine power output is found to be most sensitive to wind speed and turbulence intensity, but the relationship depends on the wind speed with respect to the turbine's rated wind speed. Shear is found to be poorly correlated to power. A machine learning method called 'regression trees' is used to create a simple model of turbine performance that could be used as part of the wind resource assessment process. This study has used simple flow fields and should be extended to more complex flows, and validated with field observations.

17 WIND ENERGY↗
DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗
DOE OSTI2019

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation: Preprint

We evaluate various classes of distributed wind turbine performance tools across two sites in the USA. The tools class ranges from the simple mass conservation model to coupled Reynolds Averaged Navier-Stokes (RANS) model, all initiated by the WIND Toolkit dataset. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100 kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document Annual Energy Production (AEP) and time series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three advance options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. The discussion includes additional values these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗
DOE OSTI2016

Atmospheric Characterization of the US Offshore Sites and Impact on Turbine Performance (Poster)

Reliable, long term offshore atmospheric data is critical to development of the US offshore wind industry. There exists significant lack of meteorological, oceanographic, and geological data at potential US offshore sites. Assessment of wind resources at heights in the range of 25-200m is needed to understand and characterize offshore wind turbine performance. Data from the US Department of Energy owned WindSentinel buoy from two US offshore sites and one European site is analyzed. Low Level Jet (LLJ) phenomena and its potential impact on the performance of an offshore wind turbine is investigated.

17 WIND ENERGY↗
DOE OSTI2015

Cooperation on Lidar for Improved Wind Turbine Performance. Cooperative Research and Development Final Report, CRADA Number CRD-13-521

Research into the use of lidar for improved wind turbine performance is an area of considerable interest. Lidars have been proposed to analyze and improve wind turbine pitch control performance, yaw alignment and control performance, as well as to improve power curve assessments. In this CRADA, NREL, NRG Systems, Inc. (“NRG”) and Avent Lidar Technology SAS (“Avent”) will collaborate on testing these concepts.

17 WIND ENERGY↗
DOE OSTI2016

Atmospheric Characterization of the US Offshore Sites and Impact on Turbine Performance

Reliable, long term offshore atmospheric data is critical to development of the US offshore wind industry. There exists significant lack of meteorological, oceanographic, and geological data at potential US offshore sites1. Assessment of wind resources at heights in the range of 25-200m is needed to understand and characterize offshore wind turbine performance. Data from the US Department of Energy owned WindSentinel buoy from two US offshore sites and one European site is analyzed. Low Level Jet (LLJ) phenomena and its potential impact on the performance of an offshore wind turbine is investigated.

offshore atmospheric data, meteorological, oceanog↗
DOE OSTI2022

Verification and Validation of Model-Scale Turbine Performance and Control Strategies for the IEA Wind 15 MW Reference Wind Turbine

To enable the fast growth of the floating offshore wind industry, simulation models must be validated with experimental data. Floating wind model-scale experiments in wind–wave facilities have been performed over the last two decades with varying levels of fidelity and limitations. However, the turbine controls in these experiments have considered only limited control strategies and implementations. To allow for control co-design, this research focuses on implementing and experimentally validating more advanced turbine control actions and strategies in a wind–wave basin for a 1:70-scale model of the International Energy Agency’s wind 15 MW reference wind turbine. The control strategies analyzed include torque control, collective pitch control, and transition region control (setpoint smoothing). Our experimental and numerical results include the effects of varying rotor speeds, blade pitches, and wind environments on the turbine thrust and torque. Numerical models from three different software tools are presented and compared to the experimental results. Their ability to effectively represent the aero-dynamic response of the wind turbine to the control actions is successfully validated. Finally, turbine controller tuning parameters based on the derivatives of thrust and torque are derived to allow for improved offshore wind turbine dynamics and to validate the ability of modeling tools to model the dynamics of floating offshore wind turbines with control co-design.

17 WIND ENERGY↗
DOE OSTI2016

Evaluation of Design & Analysis Code, CACTUS, for Predicting Crossflow Hydrokinetic Turbine Performance

CACTUS, developed by Sandia National Laboratories, is an open-source code for the design and analysis of wind and hydrokinetic turbines. While it has undergone extensive validation for both vertical axis and horizontal axis wind turbines, and it has been demonstrated to accurately predict the performance of horizontal (axial-flow) hydrokinetic turbines, its ability to predict the performance of crossflow hydrokinetic turbines has yet to be tested. The present study addresses this problem by comparing the predicted performance curves derived from CACTUS simulations of the U.S. Department of Energy’s 1:6 scale reference model crossflow turbine to those derived by experimental measurements in a tow tank using the same model turbine at the University of New Hampshire. It shows that CACTUS cannot accurately predict the performance of this crossflow turbine, raising concerns on its application to crossflow hydrokinetic turbines generally. The lack of quality data on NACA 0021 foil aerodynamic (hydrodynamic) characteristics over the wide range of angles of attack (AoA) and Reynolds numbers is identified as the main cause for poor model prediction. A comparison of several different NACA 0021 foil data sources, derived using both physical and numerical modeling experiments, indicates significant discrepancies at the high AoA experienced by foils on crossflow turbines. Users of CACTUS for crossflow hydrokinetic turbines are, therefore, advised to limit its application to higher tip speed ratios (lower AoA), and to carefully verify the reliability and accuracy of their foil data. Accurate empirical data on the aerodynamic characteristics of the foil is the greatest limitation to predicting performance for crossflow turbines with semi-empirical models like CACTUS. Future improvements of CACTUS for crossflow turbine performance prediction will require the development of accurate foil aerodynamic characteristic data sets within the appropriate ranges of Reynolds numbers and AoA.

13 HYDRO ENERGY↗
DOE OSTI2022

Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches

Abstract Wind power production is driven by, and varies with, the stochastic yet uncontrollable wind and environmental inputs. To compare a wind turbine's performance, a direct comparison on power outputs is always confounded by the stochastic effect of weather inputs. It is therefore crucial to control for the weather and environmental influence. Toward that objective, our study proposes an energy decomposition approach. We start with comparing the change in the total energy production and refer to the change in total energy as delta energy. On this delta energy, we apply our decomposition method, which is to separate the portion of energy change due to weather effects from that due to the turbine itself. We derive a set of mathematical relationships allowing us to perform this decomposition and examine the credibility and robustness of the proposed decomposition approach through extensive cross‐validation and case studies. We then apply the decomposition approach to Supervisory Control and Data Acquisition data associated with several wind turbines to which leading‐edge protection was carried out. Our study shows that the leading‐edge protection applied on blades may cause a small decline to the power production efficiency in the short term, although we expect the leading‐edge protection to benefit the blade's reliability in the long term.

17 WIND ENERGY↗
DOE OSTI2024

Wind turbine performance enhancement with minimal structural load penalty: A design philosophy

The performance benefits of using tip devices on wind turbines has been well-documented. However, previous studies show that adding blade tip devices such as winglets leads to a significant increase in blade root bending moment, potentially requiring structural reinforcement with cost and weight drawbacks. A new and unique design philosophy for retrofit blade tip devices for wind turbines is presented. By balancing generated aerodynamic and centrifugal loads, these devices offer an increase in power production without the need for structural reinforcement. Predicted performance and cost benefits of using retrofit blade tip devices on the National Renewable Energy Laboratory 5 MW reference wind turbine are shown. Finally, the addition of blade tip devices resulted in significant improvements in the coefficient of power (C p ) and annual energy production (AEP).

17 WIND ENERGY↗
DOE OSTI2015

Effect of Blade Roughness on Transition and Wind Turbine Performance.

The real-world effect of accumulated surface roughness on wind-turbine power production is not well understood. To isolate specific blade roughness features and test their effect, field measurements of turbine-blade roughness were made and simulated on a NACA 633-418 airfoil in a wind tunnel. Insect roughness, paint chips, and erosion were characterized then manufactured. In the tests, these roughness configurations were recreated as distributed roughness, a forward-facing step, and an eroded leading edge. Distributed roughness was tested in three heights and five densities. Chord Reynolds number was varied between 0:8 to 4:8 × 10 6 . Measurements included lift, drag, pitching moment, and boundary-layer transition location. Results indicate minimal effect from paint-chip roughness. As distributed roughness height and density increase, the lift-curve slope, maximum lift, and lift-to-drag ratio decrease. As Reynolds number increases, natural transition is replaced by bypass transition. The critical roughness Reynolds number varies between 178 to 318, within the historical range. At a chord Reynolds number of 3:2 × 10 6 , the maximum lift-to-drag ratio decreases 40% for 140 μm roughness, corresponding to a 2.3% loss in annual energy production. Simulated performance loss compares well to measured performance loss of an in-service wind turbine.

17 WIND ENERGY↗
DOE OSTI2017

Effect of Surface Roughness on Wind Turbine Performance

Wind farm operators observe production deficits as machines age. Quantifying deterioration on individual components is difficult, but one potential explanation is accumulation of blade surface roughness. Historically, wind turbine airfoils were designed for lift to be insensitive to roughness by simulating roughness with trip strips. However, roughness was still shown to negatively affect performance. Furthermore, experiments illustrated distributed roughness is not properly simulated by trip strips. To understand how real-world roughness affects performance, field measurements of turbine-blade roughness were made and simulated on a NACA 63 3 -418 airfoil in a wind tunnel. Insect roughness and paint chips were characterized and recreated as distributed roughness and a forward-facing step. Distributed roughness was tested in three heights and five density configurations. The model chord Reynolds number was varied between 0.8 to 4.8 x 10 6 . Measurements of lift, drag, pitching moment, and boundary-layer transition were completed. Results indicate minimal effect from paint-chip roughness. As distributed roughness height and density increase, lift-curve slope, maximum lift, and lift-to-drag ratio decrease. As Reynolds number increases, bypass transition occurs earlier. The critical roughness Reynolds number varies between 178 to 318, within the historical range. Little sensitivity to pressure gradient is observed. At a chord Reynolds number of 3.2 x 10 6 , the maximum lift-to-drag ratio decreases 40% for 140 m roughness, corresponding to a 2.3% loss in annual energy production. Simulated performance loss compares well to measured performance loss on an in-service wind turbine.

17 WIND ENERGY↗
DOE OSTI2013

Simulation of Wind Turbine Performance and Loading Patterns using a Coupled Meso-scale/Micro-scale Flow Analysis Model

HELIOS simulations were conducted for the WPS-30 turbine from the Sexbierum experimental site. The MMCI module was used to include the effects of the atmospheric boundary layer using the strategy described in the methodology section. In Figure 1 , the thrust and power variation for the WRF/Helios (Meso-scale/micro-scale simulation) is compared with measured power and stand-alone Helios computations (uniform inflow). The unsteady variations in power and thrust caused by the turbulent inflow conditions is clearly evident from these figures. Note that the mean hub height velocity could not be exactly matched to the uniform inflow values of 8 m/s because of constraints in adjusting the geostrophic wind and roughness length in Meso-scale WRF simulations. The offset in mean power and thrust notable in the figures are because of the small difference in the mean wind speed. Nonetheless, these results provide proof-of-concept of the integrated Meso-scale/Micro-scale simulation capability developed as part of this work. To further substantiate the effects of turbulent inflow, flow visualizations (iso-surfaces of vorticity overlaid on contours of velocity magnitude) are shown in Figure 2 . Meandering of the vortex wake and eventual breakdown are observed in the flow visualizations. Vortex breakdown is observed to occur between 1.5 and 2.5 diameter distance from the turbine. In uniform inflow (not shown here), the vortex wake persists to much longer distance of about 5 diameters before beginning to breakdown. It is expected that the vortex breakdown will be strongly correlated with the turbulence intensity of the inflow. The capability developed herein provides enabling technology for analyzing and characterizing such effects.

17 WIND ENERGY↗