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175 results for “wind turbine”
Radar-based sensing of wind turbines blades based on 35 GHz FMCW sensors installed at operational wind turbine towers
<p>The dataset contains radar-based measurements of rotor blades from three operational wind turbines as part of a structural health monitoring system. For this purpose, a sensor box with a 35 GHz radar sensor (about 1 000 measurements per second) and a camera system (about 100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The measurements demonstrate that the damage detection methodology can be transferred to an image processing problem. The challenge is to manage the strong influence from variable environmental and operational conditions, e.g. wind speed, azimuth orientation, that modify the rotor blade appearance in the radargram significantly. The dataset contains measurements from the intact turbine blade conditions, because it was not possible to introduce structural damage.</p>
Wind turbine condition monitoring dataset of Fraunhofer LBF
<h3>Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring (CM) applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality.</h3> <h3>See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5</h3>
Ultrasound scans of wind turbine bearings with white etching crack damage
<p>A set of ultrasound images of wind turbine bearings with white etching crack subsurface damage. Attenuation levels above -10 dB indicate subsurface damage. Five tests were run under constant loading in a laboratory test rig, with two bearings in parallel. Tests were stopped at the indicated times (in hours) when vibration levels went above a certain threshold. This typically means that one of the two bearings has failed, although this is somewhat inconclusive for the fifth test where both bearings seem to be close to failure. For each bearing two sides were scanned (indicated by A and H).</p> <p>The experiment was performed in summer-autumn 2017 by DTU Wind Energy, as part of the FP7 Integrated Research Programme in the field of Wind Energy (IRPWIND), 2nd Round of Joint Experiments.</p> <p>For more information about this dataset please contact Dr Hilmar Danielsen at DTU Wind Energy.<br> </p> <p> </p>
Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades
<p>A scout and zoom dataset including video-versions of the figures behind the following paper to where the references should be given:</p> <p>Mikkelsen, L.P. Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades, IOP Conf. Series: Materials Science and Engineering <strong>380</strong> (2018) 012006 , http://iopscience.iop.org/article/10.1088/1757-899X/388/1/012006.</p> <p>The SFoV data-set is saved as both a 3D and a 2D (zipped) tiff stack.</p>
Data set: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain
<p>Data set of Wind Energy Science (WES) paper: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain</p>
Wake data documentation for a wind turbine rotor with winglets
<p>This is the documentation of data, measured in a experimental campaign, in which the effects of winglets<br> on a model wind turbine rotor were investigated</p>
SiWiRoRa - Simulated Wind-turbine Rotor-blade Radargrams
<h1>SiWiRoRa</h1> <p>SiWiRoRa stand for '<strong>Si</strong>mulated <strong>Wi</strong>nd-turbine <strong>Ro</strong>tor-blade <strong>Ra</strong>dargrams'.</p> <h2>Overview</h2> <p>A novel kind of dataset comprised of 9504 grayscale images (quadratic, 224 px) representing simulated radargrams. SiWiRoRa enables analytical machine-learning experiments in the emerging area of radar-based computer-vision research on wind-turbine rotor blades. Here, radargrams are images showing distance on the horizontal x-axis (increasing from left to right) and time on the vertical y-axis (increasing from top to bottom) as well as reflected intensity given by the colorscale (increasing from black to white).</p> <h2>Details</h2> <p>Geometries have been modeled using Cyberbotics Webots (1188 different configurations) and stochastic elements have been added in post-processing (additional 8 independent repetitions per 1 configuration). Besides this aforementioned stochastic noise, the dataset has a full-factorial design consisting in... </p> <ul> <li>9 distinct levels of rotor speeds (clockwise rotation when viewed from the exterior onto the rotor and the radar behind it)</li> <li>11 levels of the yaw angles (between radar direction and nacelle orientation)</li> <li>6 variations of wind pressure (forcing the rotor closer to the radar)</li> <li>2 different time offsets (corresponding to alternative triggers of the radar)</li> </ul> <p>Geometries have been chosen so as to yield non-axisymmetrical radargrams (even apart from noise).</p> <h2>Acknowledgements</h2> <p>The present dataset complements and has been inspired by the field measurements published under</p> <p>https://doi.org/10.5281/zenodo.8366654</p> <p>(Hyperlink is provided in the References section).</p>
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Data from: Wind turbines in managed forests partially displace common birds
<p><span>Wind turbines are increasingly being installed in forests, which can lead to land use disputes between climate mitigation efforts and nature conservation. Environmental impact assessments precede the construction of wind turbines to ensure that wind turbines are installed only in managed or degraded forests that are of potentially low value for conservation. It is unknown, nevertheless, if animals deemed of minor relevance in environmental impact assessments are affected by wind turbines in managed forests. We investigated the impact of wind turbines on common forest birds, by counting birds </span><span>along an impact-gradient of wind turbines</span><span> in 24 temperate forests in Hesse, Germany. </span><span>During 860 point counts, we counted 2,231 birds from 45 species. Bird communities were strongly related to forest structure, season and the rotor diameter of wind turbines, but were not related to wind turbine distance. For instance, bird abundance decreased in structure-poor (-38%) and monocultural (-41%) forests with wind turbines, and in young (-36%) deciduous forests with larger and more wind turbines (-24%). Overall, our findings suggest that wind turbines in managed forests partially displace common forest birds. If these birds are displaced to harsh environments, wind turbines might indirectly contribute to a decline of their populations. Yet, forest bird communities are locally more sensitive to forest quality than to wind turbine presence. To prevent further displacement of forest animals, forests of lowest quality for wildlife should be preferred in spatial planning for wind turbines, for instance small and structure-poor monocultures along highways.</span></p>
Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine
<p>This dataset contains simulated acceleration measurements for the 5MW reference drivetrain model installed on a spar-type floating wind turbine. Measurements are one-hour simulations with a sample rate of 200 Hz. See Data description file for details.</p> <p>How to cite: Dibaj, Ali, & Nejad, Amir. (2023). Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7674842</p>
Aerodynamics of a Floating Wind Turbine Scale Model with Active Control
<p>This dataset is about the aerodynamic response of a wind turbine scale model subjected to prescribed platform pitch motion, as it would occur during normal operation of floating wind turbines. The wind turbine has active control functionalities representative of those of utility-scale turbines. The turbine controller is the Reference Open Source Controller (ROSCO), which has been implemented in MATLAB Simulink for wind tunnel testing and co-simulation with OpenFAST. The dataset contains an OpenFAST model of the scaled turbine and its controller. </p>
Ambient vibration test of wind turbine blade in OWI-lab's Large Climate Chamber
<p><strong>Ambient vibration test of wind turbine of wind turbine blade in OWI-lab's Large Climate Chamber</strong></p> <p>Selected data from the large scale icing experiment as conducted in OWI-lab's large climate chamber on 15/11/2022. Results were presented during Eurodyn 2023 in :"Large scale test of vibration based icing detection for wind turbines", Weijtjens et.al. </p> <p><em>Data is (summarized, more details are given below):</em></p> <p>- 24 Ten minute acceleration data files collected (MO04_acceleration_YYYYmmdd_HHMMSS.csv) <br> - Pictures during the experiment timestamped (local time: UTC+1)<br> - Temperature measurements of the climate chamber's inflow temperatures<br> - Modal parameter results for X and Z direction </p> <p>All times are in UTC unless mentioned otherwise.</p> <p><strong>Measurement concept</strong></p> <p>The measurement data is collected during an experiment as conducted as part of the <a href="https://www.sirris.be/nl/joint-project/fighting-icing">COOCK fighting icing </a> project led by Sirris. In the OWI-lab climate chamber a wind turbine blade was subjected to icing conditions. The test comprises the collection of ambient vibration data using three tri-axial accelerometers installed on the blade. During the day the blade is cooled and cold water is sprayed on the blade to simulate the growth of ice on the blade. The steps of the experiment are:</p> <pre><code>2022-11-15 10:04:00+00:00: Start cooling to -10°C 2022-11-15 11:23:00+00:00: Start spray 2022-11-15 12:13:00+00:00: Accelerate spray 2022-11-15 12:49:00+00:00: End of spray 2022-11-15 13:13:00+00:00: Start heating 2022-11-15 14:14:00+00:00: Start cooling to -10°C 2022-11-15 15:03:00+00:00: Start spray 2022-11-15 15:43:00+00:00: End of spray</code></pre> <p>For more information on the </p> <p><strong>Ten minute acceleration data</strong></p> <p>Twentyfour ten minute samples of the 3 accelerometers on the blade sampled at 250Hz. The data is two 2-hour blocks, one at night before the testing, the second 2 hour block is during the spraying.</p> <p>The 10.1m long blade was instrumented with three tri-axial MEMS accelerometers (Micromega IAC-UHRS-Ud-03, ±3g) on the suction side of the blade. In which the X-direction corresponded to the edgewise motion of the blade, the Z-direction to the flapwise direction and the Y-direction to the less relevant lengthwise motion. The three sensors were installed at approximately 1/4, 5/8 of the blade length and 130cm from the tip of the blade.</p> <p><strong>Modal parameter data</strong></p> <p>The resulting modal parameter data ( for the entire day of testing) , in both X and Z direction are provided in MO04_mpe_*_20221115.csv. The data has following shape:</p> <table> <thead> <tr> <th> </th> <th>mean_frequency</th> <th>std_frequency</th> <th>mean_damping</th> <th>std_damping</th> <th>size</th> <th>algorithm</th> <th>timestamp</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1.649219</td> <td>0.001952</td> <td>1.128411</td> <td>0.103419</td> <td>51</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>1</th> <td>2.028879</td> <td>0.000927</td> <td>0.432596</td> <td>0.068354</td> <td>60</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>2</th> <td>2.923999</td> <td>0.009472</td> <td>2.677156</td> <td>0.615895</td> <td>19</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>3</th> <td>3.779268</td> <td>0.003649</td> <td>3.462347</td> <td>0.715628</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>4</th> <td>5.786029</td> <td>0.005541</td> <td>0.539010</td> <td>0.261049</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> </tbody> </table> <p>In which `mean_frequency` and `std_frequency` are the cluster mean frequency, are the cluster std. frequency and cluster std. damping and the cluster size (size). The LCSF algorithm is used. The algorithm used is described in <a href="https://journals.sagepub.com/doi/abs/10.1177/1475921714556568?journalCode=shma">Source</a>.</p> <p>Note: multiple rows share one timestamp, this is because the algorithm can detect multiple modes per timestamp.</p> <p><strong>Temperature data</strong></p> <p>The inflow air temperatures are shared in a separate .csv files: ClimateChamber_20221115.csv</p> <p><strong>Pictures</strong></p> <p>Picture are collected during the test are shared. Each picture is timestamped in local time (UTC+1)</p> <p> </p>
The eco-conscious wind turbine: design beyond purely economic metrics
<p>Figures from the publication <em>The eco-conscious wind turbine: design beyond purely economic<br> metrics</em>.</p>
Noise production by a Savonius type wind turbine- a comparison between single and five segment rotor.
<p>The following data set contains acoustic evaluation results from experimental studies performed on Savonius wind turbine. </p> <p>Noise from a typical single segment Savonius wind turbine has been compared to that of its five segment counterpart. This comparison is mainly conducted for three case:</p> <p> </p> <p>1. when rotor is loaded (giving us max coefficient of performance)</p> <p>2. when the rotor is not loaded (free rotation)</p> <p>3. when the rotor is stopped. </p> <p> </p> <p>Power characteristics of the two rotors have also been plotted, allowing to establish a comparison between noise and power produced.</p> <p> </p> <p> </p> <p> </p>
zEPHYR - Noise and performance correlation of a Savonius type vertical axis wind turbine.
The presented data set consists of noise measurement and performance characteristics obtained as a part of the experimental campaign in the Open Jet Facility (OJF) at Delft University of Technology. The observations have been recorded for a typical Savonius vertical axis wind turbine. Refer to the file "setup" for further experimental campaign details.
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Data from: Wind turbines in managed forests partially displace common birds
Open the record for dataset details and reuse information.
Experimental and theoretical study of wind turbine wakes in yawed conditions
<p>Hub-height horizontal-plane PIV measurement data of the wake behind a stand-alone yawed wind turbine.</p> <p>HDF5 data structure:</p> <ul> <li>Yaw_0_Lambda_o: yaw angle (0, 10, 20, 30 degree) case at the optimal tip-speed ratio <ul> <li>u_avg: mean of u (v, w) component.</li> <li>u_std: standard deviation of the u (v, w) component.</li> <li>x: x grid point.</li> <li>y: y grid point.</li> </ul> </li> </ul> <p>Reference</p> <p>Bastankhah, Majid, and Fernando Porté-Agel. "Experimental and theoretical study of wind turbine wakes in yawed conditions." <em>Journal of Fluid Mechanics</em> 806 (2016): 506-541.</p> <p> </p>
Data from: Effect of tower base painting on willow ptarmigan collision rates with wind turbines
<p>1. Birds colliding with turbine rotor blades is a well-known negative consequence of wind-power plants. However, there has been far less attention to the risk of birds colliding with the turbine towers, and how to mitigate this risk. 2. Based on data from the Smøla wind-power plant in Central Norway, it seems highly likely that willow ptarmigan (the only gallinaceous species found on the island) is prone to collide with turbine towers. By employing a BACI-approach, we tested if painting the lower parts of turbine towers black would reduce the collision risk. 3. Overall, there was a 48% reduction in the number of recorded ptarmigan carcasses per search at painted turbines relative to neighbouring control (unpainted) ones, with significant variation both within and between years. 4. Using contrast painting to the turbine towers resulted in significantly reduced number of ptarmigan carcasses found, emphasizing the effectiveness of such a relatively simple mitigation measure.</p>
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