SMARTEOLE Wind Farm Control open dataset

<strong>Introduction</strong> 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. The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW). <strong>Description</strong> 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 : https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html. Some information can also be found in the related IEA task 44 wiki page. The following files can be found in the dataset : SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines. 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. 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. SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (WGS84) and XY metric system (French Lambert 93). 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. 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. 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. SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine. SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables. 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. <strong>Acknowledgement</strong> 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). Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available. <strong>How to cite this dataset</strong> When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication : "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)". When citing the dataset in the core text of a paper, the reference to Simley et al. can simply be used. <strong>Related datasets and publications</strong> 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. <em>SMARTEOLE Field Test 1</em> 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. Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017. 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. 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. 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. <em>SMARTEOLE Field Test 2</em> 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. 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. <em>SMARTEOLE Field Test 3</em> 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. <strong>Release Notes</strong> v1.0 (2022-11-24) : first version of the dataset.<strong>引言</strong> 本数据集源自法国国家级项目SMARTEOLE的第三次也是最后一次野外试验活动。数据集包含法国境内风电场Sole du Moulin Vieux的7台风力发电机组实测数据，本次实验开展了风电场控制现场测试，以评估尾流转向策略提升发电功率的性能。该风电场共配备7台Senvion MM82型风力发电机组（叶轮直径82米，额定功率2.05 MW）。 <strong>数据集说明</strong> 本次测试于2020年2月17日至5月25日期间开展，针对SMV6号机组实施尾流转向控制。本数据集覆盖完整测试周期，并经过预处理以简化风电场控制实验的分析工作：已剔除所有至少1台机组停机的时间步长；已校正监控与数据采集（Supervisory Control and Data Acquisition, SCADA）系统的机舱位置与风向信号，以消除北向对齐偏差；最终将原始高频传感器数据统一标准化为1分钟的时间分辨率。 如需了解本次野外试验活动的细节及数据分析中的预处理步骤，请查阅相关学术论文：https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html。相关信息亦可参考国际能源署（IEA）任务44的维基页面。 本数据集包含以下文件： 1. SMARTEOLE_WakeSteering_SCADA_1minData.csv：7台机组的监控与数据采集（SCADA）数据。 2. SMARTEOLE_WakeSteering_ControlLog_1minData.csv：部署于SMV6号机组的控制系统运行日志，记录各时间步长下机组施加的偏航偏移量。 3. SMARTEOLE_WakeSteering_WindCube_1minData.csv：部署于SMV2与SMV3之间的地基WindCube廓线激光雷达实测数据，可用于评估风电场的环境来流风况。 4. SMARTEOLE_WakeSteering_Coordinates_staticData.csv：风电场机组及WindCube的坐标文件，采用WGS84经纬度坐标系与法国Lambert 93平面直角坐标系两种格式存储。 5. SMARTEOLE_WakeSteering_Map.pdf：风电场机组与WindCube的位置分布图，与前述学术论文中附图完全一致。 6. SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv：论文中使用的传递函数文件，用于校正SMV6号机组实测风速，使其更匹配机组上游150米（约1.8倍叶轮直径）处的来流自由流风速。该传递函数通过安装于SMV6机舱顶部的激光雷达推导得到。 7. SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv：论文中使用的传递函数文件，用于推导并校正基准功率与风速信号——基准信号定义为SMV1、SMV2、SMV3及SMV7的功率与风速平均值，以消除SMV6号机组的信号偏差随风向与风速的变化。该校正后的基准信号用于量化尾流转向策略的影响效果。 8. SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv：MM82型机组标准运行模式（模式0）的额定功率与推力曲线。 9. SMARTEOLE_WakeSteering_ReadMe.xlsx：说明文档，详细解释各数据文件中不同变量的含义。 需要说明的是，SMV6号机组机舱顶部的WindCube激光雷达原始数据无法公开共享，但可通过该传感器推导得到的传递函数对SCADA通道数据进行校正。上述传递函数的推导方法已在《Wind Energy Science》期刊的相关论文中详述。 <strong>致谢</strong> 本数据集的创建依托法国国家级项目SMARTEOLE，并得到法国国家研究署（Agence Nationale de la Recherche, ANR）资助（项目编号ANR-14-CE05-0034）。此外，感谢ENGIE Green公司授权本数据集公开发布。 <strong>数据集引用方式</strong> 若在后续研究中使用本数据集，请在论文的致谢部分添加如下语句："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)"。若在论文正文核心部分引用本数据集，可直接使用Simley等人的学术文献作为参考文献。 <strong>相关数据集与学术文献</strong> SMARTEOLE项目期间共开展多次野外测试活动。尽管默认不公开这些数据集，但可根据具体项目需求并签署专项保密协议（Non-Disclosure Agreement, NDA）后共享。以下列出相关学术文献，可帮助了解各数据集的研究内容： <em>SMARTEOLE 野外试验1</em> Ahmad T.等，《风电场协同控制的现场实施与试验》，<em>IEEE Transactions on Sustainable Energy</em>，9(3)，2018，10.1109/TSTE.2017.2774508。 Duc T.，《基于创新控制策略的风电场发电量优化》，硕士学位论文，丹麦科技大学风电研究院-M-0161，2017。 Duc T.等，《局地湍流参数化改进Jensen尾流模型及其在运行风电场发电量优化中的应用》，<em>Wind Energy Science</em>，4(2)，2019，10.5194/wes-4-287-2019。 Torres Garcia E.等，《基于7个月激光雷达测量的风电机组尾流相互作用统计特性》，<em>Renewable Energy</em>，130，2019，10.1016/j.renene.2018.06.030。 Hegazy A.等，《风电机组尾流相互作用的激光雷达与SCADA数据处理及与解析尾流模型的对比》，<em>Renewable Energy</em>，181，2022，10.1016/j.renene.2021.09.019。 <em>SMARTEOLE 野外试验2</em> Tagliatti F.，《风电场控制下的风电机组疲劳载荷研究：现场实测数据分析》，硕士学位论文，丹麦科技大学风电研究院-M-0302，2019。 Göçmen T.等，《FarmConners风电场流动控制基准测试——第一部分：盲测结果》，<em>Wind Energy Science</em>，7(5)，2022，10.5194/wes-7-1791-2022。 <em>SMARTEOLE 野外试验3</em> Simley E.等，《商业风电场尾流转向试验结果：尾流转向性能的风速依赖性研究》，<em>Wind Energy Science</em>，6(6)，2021，10.5194/wes-6-1427-2021。 <strong>版本说明</strong> v1.0（2022-11-24）：本数据集的首个公开版本。