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10 results for “Wind Farm Control”
Dataset for article: Integer programming for optimal yaw control of wind farms
<div> <p>This is the dataset for the article "Integer programming for optimal yaw control of wind farms". We provide the integer programs (lp-files) and corresponding solver log files for each case of our series of experiments. Submission of manuscript: September 2024 (v1). Major revision of manuscript: February 2025 (v2). Minor revision of manuscript: April 2025 (v3).</p> </div>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments - data sets
<p>Data sets of the wind tunnel experiments described in "Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments". DOI: https://doi.org/10.5194/wes-2019-50</p>
Dataset for Article: Potential of dynamic wind farm control by axial induction in the case of wind gusts
<p>This is the dataset for the article "Potential of dynamic wind farm control by axial induction in the case of wind gusts". We provide the data as OUT-files from FAST.Farm simulation. Submission of revised manuscript: November 2023.</p>
Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations
<p>Dataset used for results in paper "Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations"</p>
SMARTEOLE Wind Farm Control open dataset
<p><strong>Introduction</strong></p> <p>This dataset is issued from the third and final field campaign of the French national project SMARTEOLE. It consists in data from 7 wind turbines of a single wind farm (Sole du Moulin Vieux, located in France) for which Wind Farm Control field tests were performed to evaluate the performance of a wake steering strategy for improving the power production.</p> <p>The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW).</p> <p> </p> <p><strong>Description</strong></p> <p>The tests were realized between 17 February – 25 May 2020, with wake steering implemented on turbine SMV6. This dataset covers this full period, and it has been pre-processed to facilitate the analysis of the Wind Farm Control experiment. All timesteps when at least one turbine was stopped were removed, and SCADA nacelle position and wind direction signals have been corrected to remove any north alignment issues. Finally, the time resolution has been standardized at 1-min from the raw data recorded at higher frequencies from the different sensors. For more details about the development of the field campaign and the pre-processing steps followed in the data analysis, please consult the related publication : <a href="https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html">https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html</a>. Some information can also be found in the related <a href="https://ieawindtask44.tudelft.nl/index.php?title=SMARTEOLE_Field_Test_3">IEA task 44 wiki page</a>.</p> <p>The following files can be found in the dataset :</p> <ul> <li>SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines.</li> <li>SMARTEOLE_WakeSteering_ControlLog_1minData.csv : logs from the control system located on turbine SMV6, responsible for the application of the wake steering. The applied yaw offset on the turbine at each timestep can be found here.</li> <li>SMARTEOLE_WakeSteering_WindCube_1minData.csv : data from the ground based WindCube profiler lidar, located between SMV2 and SMV3. This can be used to assess the ambient environmental wind conditions at the farm.</li> <li>SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (<a href="https://epsg.io/4326">WGS84</a>) and XY metric system (<a href="https://epsg.io/2154">French Lambert 93</a>).</li> <li>SMARTEOLE_WakeSteering_Map.pdf : the map of the farm showing the location of wind turbines and WindCube. This is the exact same map as the one seen in the paper indicated above.</li> <li>SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv : the transfer function used in the paper to correct the wind speed measured by SMV6 to better match the freestream wind speed at 150m upstream (i.e. approximately 1.8 diameters), derived using WindCube nacelle lidar installed on top of the turbine.</li> <li>SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv : the transfer function used in the paper to derive and correct the reference power and wind speed signals —defined as the mean values of the power and wind speeds from SMV1, SMV2, SMV3, and SMV7— to remove biases from the values at SMV6 as a function of wind direction and wind speed. These corrected reference signals are used for quantifying the impact of the wake steering.</li> <li>SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine.</li> <li>SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables.</li> </ul> <p>Unfortunately, the WindCube nacelle lidar data on top of SMV6 could not be shared, instead the transfer functions derived thanks to this sensor can be used to correct the SCADA channels. The Wind Energy Science publication describes how these transfer functions were obtained.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The creation of this dataset was realized in the scope of French national project SMARTEOLE, supported by the <em>Agence Nationale de la Recherche</em> (grant no. ANR-14-CE05-0034).</p> <p>Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available.</p> <p> </p> <p><strong>How to cite this dataset</strong></p> <p>When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication :</p> <p>"The dataset used in this research has been obtained by ENGIE Green in the scope of French national project SMARTEOLE (grant no. ANR-14-CE05-0034)".</p> <p>When citing the dataset in the core text of a paper, the reference to <a href="https://wes.copernicus.org/articles/6/1427/2021/">Simley et al.</a> can simply be used.</p> <p> </p> <p><strong>Related datasets and publications</strong></p> <p>Several field test campaigns were realized in the scope of SMARTEOLE project. Although these data are not made publicly available by default, they can be shared in a per-project basis and under the protection of a dedicated NDA. Please refer to the following publications listed below to get an idea of the content of the different datasets.</p> <p><em>SMARTEOLE Field Test 1</em></p> <ul> <li>Ahmad T. et al., Field Implementation and Trial of Coordinated Control of WIND Farms, <em>IEEE Transactions on Sustainable Energy</em>, 9(3), 2018, 10.1109/TSTE.2017.2774508.</li> <li>Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017.</li> <li>Duc T. et al., Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, <em>Wind Energy Science</em>, 4(2), 2019, 10.5194/wes-4-287-2019.</li> <li>Torres Garcia E. et al., Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign, <em>Renewable Energy</em>, 130, 2019, 10.1016/j.renene.2018.06.030.</li> <li>Hegazy A. et al., LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models, <em>Renewable Energy</em>, 181, 2022, 10.1016/j.renene.2021.09.019.</li> </ul> <p><em>SMARTEOLE Field Test 2</em></p> <ul> <li>Tagliatti F., Investigation of Wind Turbine Fatigue Loads under Wind Farm Control: Analysis of Field Measurements, Master’s thesis, DTU Wind Energy-M-0302, 2019.</li> <li>Göçmen T. et al., FarmConners wind farm flow control benchmark – Part 1: Blind test results, <em>Wind Energy Science</em>, 7(5), 2022, 10.5194/wes-7-1791-2022.</li> </ul> <p><em>SMARTEOLE Field Test 3</em></p> <ul> <li>Simley E. et al., Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, <em>Wind Energy Science</em>, 6(6) 2021, 10.5194/wes-6-1427-2021.</li> </ul> <p> </p> <p><strong>Release Notes</strong></p> <ul> <li>v1.0 (2022-11-24) : first version of the dataset.</li> </ul>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method
<p><span>With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.</span></p>
Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach
<p>Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.</p>
Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method
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