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175 results for “wind turbine”
Hovione Wind Turbine: bird survey datasets, summer 2024
<p>Bird surveys carried out to supplement the Environmental Impact Assessment Report and Natura Impact Statement for a proposed wind turbine at the Hovione Cork site, Ringaskiddy, Co. Cork, Ireland</p>
Wind tunnel experiments on wind turbine wakes in yaw
<p>This data set contains Laser Doppler Anemometer measurements in the wake behind two different model wind turbines, recorded in a wind tunnel campain at the NTNU in Trondheim. Full plane wake data were recorded, with a focus on the effect of yaw misalignment and inflow turbulence. Please refer to the documentation document for more information.</p>
Supplemental Material to Journal Article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades"
<p>This set supplements the figure data to the article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades", DOI: <a href="http://doi.org/10.2514/1.J058179">10.2514/1.J058179</a>.</p>
Supplemental Material to Article "Development of thermal residual stresses during manufacture of wind turbine blades"
<p>This set supplements the figure data to the article "Development of thermal residual stresses during manufacture of wind turbine blades", DOI: .</p>
Multidirectional load multipliers: application for shared anchors for floating wind turbines
<div> <h1>Multidirectional load multipliers</h1> </div> <p>The load multipliers presented in this dataset are based on the multiline anchor loads calculated for a spar floating wind turbine presented by:</p> <p>Balakrishnan, K., Arwade, S.R., DeGroot, D.J., Fontana, C., Landon, M., Aubeny, C.P., 2020. <em>Comparison of multiline anchors for offshore wind turbines with spar and with semisubmersible</em>. Journal of Physics: Conference Series, <strong>1452</strong>, 012032. <a href="https://doi.org/10.1088/1742-6596/1452/1/012032" rel="nofollow">doi:10.1088/1742-6596/1452/1/012032</a>.</p> <div> <h2>Files</h2> </div> <p>Load multipliers for three load cases grouped in Wind Wave and Current directions. Those are the loads arriving at a multiline anchor.</p> <div> <h3>DLC 0WWC: Wind Wave and Current at 0 degrees</h3> </div> <ul> <li>DLC_0deg_push.txt</li> <li>DLC_0deg_push2.txt</li> <li>DLC_0deg_push3.txt</li> <li>DLC_0deg_step.txt</li> <li>DLC_0deg_step2.txt</li> <li>DLC_0deg_step3.txt</li> </ul> <div> <h3>DLC 30WWC: Wind Wave and Current at 30 degrees</h3> </div> <ul> <li>DLC_30deg_push.txt</li> <li>DLC_30deg_push2.txt</li> <li>DLC_30deg_push3.txt</li> <li>DLC_30deg_step.txt</li> <li>DLC_30deg_step2.txt</li> <li>DLC_30deg_step3.txt</li> </ul> <div> <h3>DLC 60WWC: Wind Wave and Current at 60 degrees</h3> </div> <ul> <li>DLC_60deg_push.txt</li> <li>DLC_60deg_push2.txt</li> <li>DLC_60deg_push3.txt</li> <li>DLC_60deg_step.txt</li> <li>DLC_60deg_step2.txt</li> <li>DLC_60deg_step3.txt</li> </ul>
Supplemental Material to Journal Article "Effect of individual blade pitch angle misalignment on the remaining useful life of wind turbines"
<p>This set supplements the figure data to the article "Effect of individual blade pitch angle misalignment on the remaining useful life of wind turbines", DOI: <a href="http://doi.org/10.5194/wes-6-1079-2021">10.5194/wes-6-1079-2021</a>.</p>
High vulnerability of juvenile Nathusius' pipistrelle bats (Pipistrellus nathusii) at wind turbines
<p>Large numbers of bats are killed by wind turbines globally, yet the specific demographic consequences of wind turbine mortality are still unclear. In this study, we compared characteristics of Nathusius’ pipistrelles (<em>Pipistrellus nathusii</em>) killed at wind turbines (N = 119) to those observed within the live population (N = 524) during the summer migration period in Germany. We used generalised linear mixed effects modelling to identify demographic groups most vulnerable to wind turbine mortality, including sex, age (adult or juvenile), and geographic origin (regional or long-distance migrant; depicted by fur stable hydrogen isotope ratios). Juveniles contributed with a higher proportion of carcasses at wind turbines than expected given their frequency in the live population suggesting that juvenile bats may be particularly vulnerable to wind turbine mortality. This effect varied with wind turbine density. Specifically, at low wind turbine densities, representing mostly inland areas with water bodies and forests where Nathusius’ pipistrelles breed, juveniles were found more often dead beneath turbines than expected based on their abundance in the live population. At high wind turbine densities, representing mostly coastal areas where Nathusius’ pipistrelles migrate, adults and juveniles were equally vulnerable. We found no evidence of increased vulnerability to wind turbines in either sex, yet we observed a higher proportion of females than males among carcasses as well as the live population, which may reflect a female bias in the live population most likely caused by females migrating from their north-eastern breeding areas migrating into Germany. A high mortality of females is conservation concern for this migratory bat species because it affects the annual reproduction rate of populations. A distant origin did not influence the likelihood of getting killed at wind turbines. A disproportionately high vulnerability of juveniles to wind turbine mortality may reduce juvenile recruitment, which may limit the resilience of Nathusius’ pipistrelles to environmental stressors such as climate change or habitat loss. Schemes to mitigate wind turbine mortality, such as elevated cut-in speeds, should be implemented throughout Europe to prevent population declines of Nathusius’ pipistrelles and other migratory bats.</p> <p> </p> <p>Supplementary material 1: DataS1: primary_data_Kruzynski_et_al (includes bats raw data of stable isotopes).</p> <p>Supplementary material 2 includes Fig S1: Hydrogen isotopic ratios of long-distance and regional Nathusius’ pipistrelle bats from carcasses (WT) and live population (BB) in Germany during the migratory period </p>
Dataset and Code for "A case study of space-time performance comparison of wind turbines on a wind farm"
<p>This is the computer code and partial dataset for producing the results in the paper, Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” <em>Renewable Energy</em>, Vol. 171, pp. 735-746 .</p> <p>[<strong>Note 1</strong>: In the Reproducibility Report, The table numbers are off by one, namely that Table 2 should be Table 3, and Table 3 should be Table 4.]</p> <p>[<strong>Note 2:</strong> The results in Table 4 included in the paper were produced by the DSWE version 1.3.4. Those results are still reproducible if using the same version of DSWE. Since then, DSWE went through some changes and updates. When using DSWE 1.5.1 version (the latest version as of May 10, 2022) and setting the optimization method to 'L-BFGS-B' (because version 1.3.4’s default optimization setting was 'L-BFGS-B'), the results corresponding to the second row in Table 4 are somewhat different. For the specific results using DSWE 1.5.1, please see the note section at the end of the <a href="https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2022/05/J77_Reproducibility_Report_v2.pdf">updated Reproducibility Report</a>.]</p>
Dataset and Code for "Bayesian spline method for assessing extreme loads on wind turbines"
<p>Here are two files. One file contains the datasets and the other contains the computer code used to generate the results in the paper, Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” <em> Annals of Applied Statistics</em>, Vol. 7, pp. 2034-2061.</p>
Dataset for "Kernel Plus method for quantifying wind turbine upgrades"
<p>This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, “Kernel Plus method for quantifying wind turbine upgrades,” <em>Wind Energy</em>, Vol. 18, pp. 1207-1219.</p>
Corrigendum of "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime" and updated dataset
<p>This data accompanies the paper "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime", published in the 18th Brazilian Congress of Thermal Sciences and Engineering (2020): <a href="http://www.sistema.abcm.org.br/articleFiles/download/28958">http://www.sistema.abcm.org.br/articleFiles/download/28958</a>. It includes the corrected integral load values described in the PDF document.</p>
Supplementary Files: Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow using Proper Orthogonal Decomposition
<p>Gappy POD reconstruction of the line-of-sight velocities from a nacelle-mounted scanning SpinnerLidar with 30% missing data for two LES cases.</p>
Variable Geometry Turbines Industrialization for a Circular Economy of Distributed Wind Energy
<p>In this work we present a novel VAWT with passive variable geometry (PVG), which combines variable radius and variable pitch of the free blades. The turbine was optimized through extensive numerical simulations, with a final design featuring short start-up time, high steady-state efficiency, low construction complexity, and low material costs. A crucial reduction of nearly 50% in the blades' mass was obtained through the use of compression moulded composites. A full-scale physical prototype was tested in a dedicated wind tunnel facility, where the typical intermittent air flow of the UBL was replicated. The turbine was connected to standard power electronics found in the solar photovoltaic market. The measured electrical power output closely matches the numerical simulations, suggesting that our PVG design can achieve a 64% increase in the capacity factor compared to a fixed-geometry turbine. Thanks to this engineering breakthrough we believe that, for the first time, wind installations in the UBL can be competitive on the energy market. We conclude our contribution by discussing industrialization and sustainability aspects of our PVG-VAWT technology, and by projecting a deployment scenario towards the uptake of a distributed urban wind market.</p>
Data from: Sensitivity analysis of collision risk at wind turbines based on flight altitude of migratory waterbirds
<p>This dataset contains information on the distribution of geese and swans and the three-dimensional flight trajectories. The former was obtained through vehicle field surveys, interviews, and a literature review. The latter was obtained using ornithodolites.</p>
META-DATA for IEA Wind Task 46. WP2: Atmospheric drivers of wind turbine blade leading edge erosion: Ancillary variables
<p>Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) created Task 46 to undertake cooperative research in the key topic of blade erosion.</p> <p>This report is a product of WorkPackage 2 <strong>Climatic conditions driving blade erosion. </strong></p> <p>The objectives of the work summarized in this report are to:</p> <ul> <li>Summarize efforts to elucidate critical atmospheric co-stressors that may accelerate leading edge erosion and hence for which meta-data regarding observations should be collated.</li> <li>Briefly describe and summarize additional data pertaining to those LEE co-stressors from sites that were the focus of analyses of hydrometeors in the report “Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors” (Pryor et al. 2021)</li> </ul> <p>Accompanying this report is a detailed spreadsheet that summarizes the meta-data regarding these co-stressor variables.</p>
Multi-topography dataset for wind turbine detection from remote sensing image
<p>The land remote sensing wind turbine dataset has 1270 remote sensing images and contains 4459 individual wind turbines. The images are taken over a large time span and contain remote sensing images of the same wind farm at different times. The dataset has both YOLO and VOC tagging formats. The dataset can be divided into five categories based on the land background: sandy land, forest land, grassland, snow land, and wasteland. Rich land background can improve the robustness of the model, and different marker formats and a large number of wind turbine individuals can meet the object detection of different models. Affected by the size of different power wind turbines, the multi-angle imaging characteristics of remote sensing satellites, the different solar radiation angles in different seasons and vegetation shading, wind turbines show large differences in the images. The image features of the wind turbine shadow are more obvious than those of the wind turbine body, so in order for the detection model to better identify the wind turbine, we label the wind turbine body and the wind turbine shadow as a whole when using the labelImg tool for labeling the wind turbine target.</p>
Sentinel-2 Wind Turbine Images with Spin and Spin Quality Labels
<p>Reproduction Data for training a classifier on the Sentinel-2 Band Offset for motion detection.</p>
Supplemental Material to Article "Quantification of process-induced effects on fatigue life of short-glass-fiber-filled adhesive used in wind turbine rotor blades"
<p>This set supplements the figure data to the article "Quantification of process-induced effects on fatigue life of short-glass-fiber-filled adhesive used in wind turbine rotor blades", DOI: xxx</p>
One year time series of relative electric (onshore and offshore) wind turbine power datasets
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric wind turbine (WT) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Flanders (onshore) and Belgium (offshore) as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric WT power was derived by dividing the measured electric WT power by the monitored peak electric WT power multiplied by 100 %.</p>
Database of Short Term Damage Equivalent Loads (DEL) of IWT7.5MW wind turbine depending on wind, TI, yaw and derating
<p>Two datasets for short term damage equivalent loads (DEL) and energy production of the IWT7.5 MW research wind turbine (https://doi.org/10.24406/IWES-N-518562) are provided. dataset_Derating is a subset of dataSet_Derating_withYaw.</p> <p>The damage equivalent loads are computed with the rainflow counting algorithm applied to time series loads. They are based on Miner's rule. Further details can be found in: <br> Requate, N., Meyer, T., and Hofmann, R.: From wind conditions to operational strategy: Optimal planning of wind turbine damage progression over its lifetime, Wind Energ. Sci., accepted for publication, 2023. </p> <p>In the paper, the dataset_Derating was used to create surrogate models.</p> <p>A fullfactorial approach to simulate different parameter-combination was applied. <br> Information about the signals and units is provided in the yml files.<br> The range of parameters is given by python code in the yml file. <br> For each set of parameters, 6 different 10 minute-realizations of a turbulent wind field ("seeds") were created. The turbine was then simulated separately for each turbulent wind field.<br> Each folder contains the short term DELs for each 10 minute simulation and the aggregated short term DELs over all 6 wind realizations.</p> <p>Outliers from simulation error were removed.<br> The data was originally intended to create surrogate models of the variation of outputs values throughout the parameter space. Further usage is possible and encouraged.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.