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175 results for “wind turbine”
Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer
<p>1) Files.csv : Dataset acquired on the experimental laboratory setup used for the emulation of a Ducted Horizontal Axis Wind Turbine.</p> <p>2) DHAWT.xlsx : aerodynamic characteristics of the Ducted Horizontal Axis Wind Turbine chosen as case of study.</p>
PIV data of straight blade and swept blade wind turbine
<p>In large wind farms, the wake behind the upstream wind turbine affects the performance of the downstream turbines which reduces the overall power output. The geometry of the wind turbine blades has significant effects on the mechanical efficiency of this process. Here, we suggest to utilize a bio-inspired blade based on the common swift wing. Common swift is known to be a long-distance flyer, able to stay aloft for long periods of time by maintaining high lift and low drag. We study the near wake flow characteristics of a horizontal turbine model with swept blades and its aerodynamic loads. These are compared with a straight-bladed turbine. The experiments were conducted in a water flume using particle image velocimetry (PIV) technique. Both blades were studied for four different speeds with freestream Reynolds numbers ranging from 23,000 to 41,000. Our results show that the near wake developed behind the swept-back blade was significantly different from the straight blade configuration. The near wake developed behind the swept-back blade exhibited relatively lower momentum loss and suppressed turbulent activity (mixing and production) compared to the straight blade. Comparing the aerodynamic characteristics, though the swept-back blade generated relatively less lift than the straight blade, the drag was relatively lower as well. Thus, the swept-back blade produced 2-3 times higher lift-to-drag ratio than the straight blade. Based on the observations, we suggest that, with improved design optimizations, using the swept-back configuration in wind turbine blades can positively help to improve the energy loss in downstream wind turbines in wind farms and can increase the overall energy efficiency.</p>
Data from: A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor
<p><span><span><span><span><span><span><span><span><span><span><span>1. With the rapid growth of wind energy developments worldwide, it is critical that the negative impacts on wildlife are considered and mitigated. This includes minimising the numbers of large soaring raptors which are killed when they collide with wind turbines.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. To reduce the likelihood of raptor collisions, turbines should be placed at locations which are least used by sensitive species. For resident or breeding species, this is often delineated crudely through the use of circular buffers centred on nest sites, which assume uniform habitat use around a nest site.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Using GPS tracking data together with a digital elevation model we build and cross-validate a simple generalizable model, to classify the spatial likelihood of wind turbine collisions for resident adult Verreaux's eagles in any landscape where there are known nests. We apply our methods to operational developments in South Africa to validate the model and demonstrate its ability in predicting actual collision mortalities.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Our Collision Risk Potential (CRP) model included the variables distance to nest, distance to conspecific nest, slope, distance to slope and elevation. Using our model, rather than a circular buffer, resulted in ca. 4–5% improvement in eagle protection while excluding development from the same amount (but not shape) of area. For an equal level of eagle protection, our model can make ca. 20–21% more area available for wind energy development compared to a circular buffer.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>5. Exploring collisions at operational wind farms in South Africa we show that our CRP model correctly predicted 87% of known collisions, while circular buffers (5.2km radius) only captured 50% of collisions.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>6. <i>Synthesis and applications</i>: We show that by using predictive models to account for habitat use, a greater area of land can be made available for wind energy development without increased mortality risk to raptors. Our predictive model can be used to provide robust guidance on wind turbine placement in South Africa in a way which minimizes the conflict between a vulnerable raptor species and the development of renewable energy. </span></span></span></span></span></span></span></span></span></span></span></p>
Site-specific Design Load Cases for floating offshore wind turbine applications I : Historical data
<p>This document includes a brief description of the <a href="https://leopard.tu-braunschweig.de/receive/dbbs_mods_00077703" target="_blank" rel="noopener">first database</a> on the site-specific Design Load Cases (DLCs) based on historical metocean data. The dataset includes metocean data, statistical analysis and site-specific DLCs across the three areas of study defined in the INF4INiTY project: Scottish Sea, Baltic Sea and Adriatic Sea. In addition to the dataset, this deliverable includes a Graphical User Interface (GUI) for the analysis of specific locations within these three areas and the generation of the site-specific DLCs.<br>The aim of this initial version of the database is to provide a first characterisation of the areas of interest in order to use the DLCs on the design of the different innovations planned in various work packages (WPs) INF4INiTY. As the project proceeds, the second database will extend the site-specific DLCs including forecasted data for different horizons and under diverse climate change scenarios.<br>The deliverable is divided into six brief sections describing the (i) the GUI, (ii) characteristics of the data, (iii) the three areas of study and technological requirements, (iv) historical metocean data, (v) site-specific statistical analysis and reporting, and (vi) site-specific DLCs.</p>
Behavioral patterns of bats at a wind turbine events
<p>Bat fatalities at wind energy facilities in North America are predominantly comprised of migratory, tree-dependent species, but it is unclear why these bats are at higher risk. Factors influencing bat susceptibility to wind turbines might be revealed by temporal patterns in their behaviors around these dynamic landscape structures. In northern temperate zones fatalities occur mostly from July through October, but whether this reflects seasonally variable behaviors, passage of migrants, or some combination of factors remains unknown. In this study, we examined video imagery spanning one year in Colorado to characterize patterns of seasonal and nightly variability in bat behavior at a wind turbine. We detected bats on 177 of 306 nights representing approximately 3,800 hours of video and > 2,000 discrete bat events. We observed bats approaching the turbine throughout the night across all months during which bats were observed. Two distinct seasonal peaks of bat activity occurred in July and September, representing 30% and 42% increases in discrete bat events from the preceding months June and August, respectively. Bats exhibited behaviors around the turbine that increased in both diversity and duration in July and September. The peaks in bat events were reflected in chasing and turbine approach behaviors. Many of the bat events involved multiple approaches to the turbine, including when bats were displaced through the air by moving blades. The seasonal and nightly patterns we observed were consistent with the possibility that wind turbines invoke investigative behaviors in bats in late summer and autumn coincident with migration, and that bats may return and fly close to wind turbines even after experiencing potentially disruptive stimuli like moving blades. Our results point to the need for a deeper understanding of the seasonality, drivers, and characteristics of bat movement across spatial scales.</p>
Wind turbine wake flight trials
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Supporting data files for simulations in "Wind fields in Category 1-3 tropical cyclones are not fully represented in wind turbine design standards"
<p>This deposit contains the time-series output from the WRF-LES simulations of three tropical cyclones used in "Wind fields in Category 1-3 tropical cyclones are not fully represented in wind turbine design standards".</p>
First Altitude-Triggered Lightning Experiment Associated with an Elevated Wind Turbine Blade on the Ground
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Experimental Investigation of Surface Roughness Effects and Transition on Wind Turbine Performance
<p>Aerodynamic experiments have been executed in the wind tunnel and on a wind turbine blade to measure the impact of roughness on the airfoil characteristics and the associated effect on rotor performance and to establish the transition location on a rotating blade. The wind tunnel tests have been performed in the low-speed, low-turbulence wind tunnel of TUDelft. The wind turbine tests were carried out at ECN&rsquo;s Wind Turbine Test Site. Roughness simulation material has been installed on the airfoil leading edge to measure the impact on airfoil performance. Microphones were mounted on the airfoil surface to detect the boundary layer laminar to turbulent transition position both on the wind tunnel model and on the wind turbine blade.</p>
Wind turbine wake flight experiment raw data
<p>Raw data from wind turbine wake flight experiments.</p>
Eagles enter rotor-swept zones of wind turbines at rates that vary by turbine
<p>There is increasing pressure on wind energy facilities to manage or mitigate for wildlife collisions. However, little information exists regarding spatial and temporal variation in collision rates, meaning that mitigation is most often a blanket prescription. To address this knowledge gap, we evaluated variation among turbines and months in an aspect of collision risk—probability of entry by an eagle into a rotor swept zone (hereafter, 'probability of entry'). We examined 10,222 eagle flight paths identified and recorded by an automated bird monitoring system at a wind energy facility in Wyoming, USA. Probabilities of entry per turbine-month combination were 4.03 times greater in some months than others, ranging 0.15 to 0.62. The overall probability of entry for the riskiest turbine (i.e. the one with the greatest probability of entry) was 2.39 times greater than the least-risky turbine. Our methodology describes large variation across turbines and months in the probability of entry. If subsequently combined with information on other sources of variation (i.e. weather, topography), this approach can identify risky versus safe situations for eagles under which cost of management, curtailment prescriptions, and collision risk can be simultaneously minimized.</p>
Operation SCADA Dataset of an Urban Small Wind Turbine in São Paulo, Brazil
<p>The dataset file contains data regarding the electrical and mechanical operational actual quantities and parameters obtained and recorded by the internal inverter controller of a Skystream 3.7 small wind turbine (SWT) installed on the roof of the High Voltage Laboratory at the Institute of Energy and Environment (IEE) of the University of Sao Paulo (USP), Brazil, recorded from 2017 to 2022.</p> <p>The main electrical parameters are the energy, voltages, and currents in the connection grid point and power frequency. Mechanical information can be retrieved, such as the rotation and the wind speed. The temperature, measured in some location points to the nacelle and inverter, is also recorded. Several other parameters concerning the SWT inverter operation, such as the dc voltages on its internal bus, alarms, and flags, are also presented.</p> <p>The files in the dataset are named as "data_swt_iee_usp_YYYY.csv" where YYYY is the referring year. In the files, the semicolon symbol (;) is used as a column separator, while the dot symbol (.) represents the decimal separator. The first row of the CSV file corresponds to the header row to help identify data as described in the file "data_description.txt". Sampling rate one record per minute.</p> <p>The first line on each year-based file represents the header table description of the data columns.</p> <p>The complete and detailed information about the installation, localization,and analysis is in the article published at: <a href="https://doi.org/10.3390/wind2040037">https://doi.org/10.3390/wind2040037</a></p> <h3><strong>LIST OS STATUS CODES - Skystream 3.7</strong></h3> <p><strong>Premise:</strong> Binary logic, where each status is represented by a specific bit within an integer value.</p> <table> <tbody> <tr> <th><strong>Numeric Code</strong></th> <th><strong>Turbine Status</strong></th> <th><strong>Grid Status</strong></th> <th><strong>System Status</strong></th> </tr> </tbody> <tbody> <tr> <td>0</td> <td>Normal. Run with energy generation</td> <td>Normal (No faults detected)</td> <td>Normal (System operating without errors)</td> </tr> <tr> <td>1</td> <td>Low Windspeed</td> <td>L1 Low Voltage</td> <td>HS Backoff</td> </tr> <tr> <td>2</td> <td>Braking</td> <td>L1 High Voltage</td> <td>SIP TX Too Long</td> </tr> <tr> <td>4</td> <td>Overspeed</td> <td>L2 Low Voltage</td> <td>Improper Reset</td> </tr> <tr> <td>8</td> <td>No Stall (Normal Operation)</td> <td>L2 High Voltage</td> <td>Battery Timeout</td> </tr> <tr> <td>16</td> <td>High Wind Test</td> <td>Offset Limit</td> <td>Drive Off</td> </tr> <tr> <td>32</td> <td>Anemometer mode</td> <td>Phase Error</td> <td>Slave Shutdown</td> </tr> <tr> <td>64</td> <td>Ramp</td> <td>Frequency Low</td> <td>Temp Shutdown</td> </tr> <tr> <td>128</td> <td>TSR Incr</td> <td>Frequency High</td> <td>High Temp</td> </tr> <tr> <td>256</td> <td>Power High</td> <td>DPLL Unlock</td> <td>Run (Normal Operation)</td> </tr> <tr> <td>512</td> <td>TSR Limit</td> <td>Grid Disconnect</td> <td>Disabled</td> </tr> <tr> <td>1024</td> <td>Quiet</td> <td>Anti-Islanding</td> <td>Waiting</td> </tr> <tr> <td>2048</td> <td>Incr Delay</td> <td> </td> <td>Temp Backoff</td> </tr> <tr> <td>4096</td> <td>RPM Control</td> <td> </td> <td>Bad Setpoints</td> </tr> <tr> <td>8192</td> <td>Vin High</td> <td> </td> <td>Bad CRC</td> </tr> </tbody> </table> <p> </p> <p><strong>These codes can represent cumulative causes, adding values when multiple causes occur.</strong></p> <p>EXAMPLES:</p> <p>Turbine status = 0 => Generating/Run</p> <p>Turbine status = 1 => Low Windspeed</p> <p>Turbine status = 3 => Low Windspeed and Braking</p> <p>Turbine status = 9 => Low Windspeed and No Stall</p> <p>Turbine status = 33 => Anemometer mode and Low Windspeed</p> <p>System status = 1024 => Waiting</p> <p>System status = 256 => Run</p> <p>Grid status = 512 => Grid Disconect</p>
On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project - Supplementary material
<p>This link stores the supplemenary material for the paper: "On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project" published on Wind Energy Science. The pdf file contains the plots for all the investigated metrics analysed in this work, which could not be reported in the manuscript due to space constraints. Further details about these results can be found in the original publication. </p>
Data from: Wind turbine blade shear web disbond detection using rotor blade operational sensing and data analysis
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Data from: A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor
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Behavioral patterns of bats at a wind turbine events
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Eagles enter rotor-swept zones of wind turbines at rates that vary by turbine
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PIV data of straight blade and swept blade wind turbine
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Vortex interaction in the wake of a two- and three-bladed wind turbine
<p>Here, the rotor design specifications + experimental results from the article "Vortex interaction in the wake of a two- and three-bladed wind turbine" (Journal of Physics, Conference Series, EERA Deepwind 2020) authored by Jan Bartl, Thomas H. Hansen, W. Ludwig Kuhn, Franz Mühle and Lars Sætran are documented.</p>
Supplemental Material to Article "Stress-based assessment of the lifetime extension for wind turbines"
<p>This set supplements the figure data to the article "Stress-based assessment of the lifetime extension for wind turbines", DOI: .</p>
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