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261 results for “Turbine”
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: Performance characteristics and bluff-body modeling of high-blockage cross-flow turbine arrays with varying rotor geometry
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Data from: Experimental validation of a linear momentum and bluff-body model for high-blockage cross-flow turbine arrays
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Data from: Wind turbines in managed forests partially displace common birds
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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>
Supplemental Material to Article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades"
<p>This set supplements the figure data to the article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades", DOI: .</p>
Insect pollinator behavior as a function of distance to the nearest turbine
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Wind Turbine SCADA Data For Early Fault Detection
<p>This dataset is published together with the <a href="https://doi.org/10.3390/data9120138">paper</a> "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. </p> <p>The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines<br>from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. </p> <p>The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.</p> <p>The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), <br>and consists of 5 wind turbines of an onshore wind farm in Portugal. <br>It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. <br>From this data 22 datasets were selected to be included in this data collection. <br>The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.<br>Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files</p> <p>More detailed information can be found in the included README-file.</p> <p><strong>Notes</strong></p> <p>In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.</p> <p><strong>Version Changes:</strong></p> <p><em>Version 5 -> 6:</em></p> <ul> <li>Changed unit of sensor_40 and sensor_61 for wind farm C to hPa instead of bar. This unit error became obvious when looking at the data and comparing it to the standard air pressure.</li> <li>Edited event_description of events 34, 7 and 19 to high temperature in transformer cell.</li> <li>Changed date in event description of event 44 since it was not affected by the change in the date anonymization procedure from version 2.</li> <li>Changed date in event description of event 47 since it was not affected by the change in the date anonymization procedure from version 2 and edited the description text</li> <li> Changed date format in event_info files to match the date format in the dataset files.</li> <li>Fixed typo in Readme</li> <li>Re-added Readme files</li> </ul> <p><em>Version</em> 4->5:</p> <p>Corrections to labels were made:</p> <ul> <li>Previously missing status_type_id 4 labels were added to datasets in Wind Farm A. </li> <li>Event 51 from Wind Farm A was wrongly labeled as a normal event. With the newly added status_type_id 4 occurences, it is to be considered an anomaly event due to a gearbox bearing damage within the prediction data.</li> <li>Wind Farm A no longer contains status_type_id 5. All occurences of status_type_id 5 have been changed to 0 and are considered normal time stamps. This change is done, because status_type_id 5 was set as a result of a wind speed and power analysis, flagging potential anomalous data. This is not based on a fixed ground truth, so status_type_id 5 was removed. For Wind Farms B and C status_type_id 5 is still valid since it is based on real SCADA-status codes.</li> <li>The event_info.csv files now contain an additional column 'asset_id'.</li> </ul> <p><em>Version 3->4:<br></em></p> <ul> <li>The change of the timestamp anonymization lead to duplicate timestamps when transitioning from a leap year to 2022. This is now fixed in Version 4.</li> </ul> <p><em>Version 2->3:</em></p> <ul> <li>In version 2 timestamp changes were not consistent with the timestamps in the event-info-files. Version 3 fixes this.</li> </ul> <p><em>Version 1->2:<br></em></p> <ul> <li>Version 2 contains one deviation from version 1 regarding the anonymization procedure. Instead of shifting the timestamps of each sub-dataset by a random number of years, the size of the time shift is now determined to be the number of years so that each sub-dataset starts in 2022. This change is made to make the timestamp anonymization more consistent and to avoid future timestamps being present within the data.</li> </ul>
Simulation data and surrogate model for the DTU 10MW reference wind turbine including down-regulation, power boosting and individual blade control
<p>This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The <em>constTSR</em> trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the <em>lin70</em> trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached.</p> <p>The regression is done with two methods: a spline-based interpolation and a Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file.</p> <p>The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (<a href="http://dx.doi.org/10.18419/opus-13959">http://dx.doi.org/10.18419/opus-13959</a>) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' <a href="https://doi.org/10.3390/en17061284">(https://doi.org/10.3390/en17061284</a>). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications. </p>
Forest gaps around wind turbines attract bat species with high collision risk
<p><span>Globally, renewable energy is highly demanded, leading to a recent increase in the number of wind turbines at forested sites. For the deployment of turbines, forest areas must be cleared, which results in significant habitat changes. To assess the ecological consequences of these changes for forest-associated bats, we quantified the activity of three bat foraging guilds at turbine clearings, adjacent forest edges, and nearby forest canopies. Specifically, open-space and edge-space foraging bats were more active at turbine clearings and forest edges than at forest canopies. Narrow-space foragers were also more often recorded at turbine clearings than at forest canopies. An increased activity of open- and edge-space foragers at turbine clearings may increase the risk for casualties. </span><span>Therefore, if turbines need to be placed in forests, strict curtailment schemes should be practiced to mitigate collision risk for these bats. This may impair the efficacy of wind energy production at forested sites.</span></p>
Forest bat activity declines with increasing wind speed in the proximity of operating wind turbines
<p>The increasing use of onshore wind energy is leading to an increased deployment of wind turbines in structurally rich habitats such as forests. Forest-affiliated bats, in turn, are at risk of colliding with the rotor blades. Due to the legal protection of bats in Europe, it is imperative to restrict the operation of wind turbines to periods of low bat activity to avoid collisions. However, bats have also been observed to avoid wind turbines over several hundred meters distance, indicating a displacement that cannot solely be explained by modifications to the habitat. This avoidance suggests a displacement of bats by indirect factors related to wind turbine operation, e.g., wake turbulences and noise emissions. Therefore, we investigated whether the activity of forest-affiliated bats is influenced by operation mode (on/off) under variable wind conditions along transects from 80 to 450 m distance to wind turbines. We divided recordings by foraging guild, i.e., either narrow-space (<em>Myotis</em>, <em>Plecotus)</em>, edge-space (<em>Pipistrellus, Barbastella</em>), or open-space foraging bats (<em>Nyctalus, Eptesicus, Vespertilio</em>), and analyzed the effects of wind turbine operation and wind speed on the recorded bat guild activity with mixed effects models. The acoustic activity of narrow-space foraging bats decreased by 91% with increasing wind speed when wind turbines were operating, while bat activity remained unaffected by wind speed when turbines were not operating. This was neither observed for open-space foraging bats nor for edge-space foraging bats, and neither wind turbine operation nor wind speed (ranging between 0 – 4 m/s at 10 m height above ground) were found to affect bat activity when considered alone. Wind turbine noise emissions are known to increase with rotor speed and consequently, wind speed, thus presenting a likely explanation for the interactive negative effect of turbine operation and wind speed specifically on noise-sensitive narrow-space foraging bats. To understand potential ecological long-term consequences for bat populations in forest areas with wind turbines and to design effective conservation measures, future research should focus on disentangling the effects of different disturbances related to turbine operation.</p>
Velocity field of "Toward ultra-efficient high fidelity predictions of wind turbine wakes"
<p>This data collection contains the velocity field obtained from VFS-Wind LES simulations, FLORIS v3.4 GCH-model and the new ML model.</p>
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