Experimental study on wind field of three-dimensional wake model considering the influence of wind shear
-
摘要: 针对目前风力机尾流模型只能描述远尾流区域的尾流分布而忽略了近尾流区域的尾流特征的问题,该文基于双高斯函数,利用流量守恒定理并通过旋转修正推导了一个新的三维尾流模型。该尾流模型考虑了风切变的影响,并且能够描述近尾流区域以及远尾流区域的三维尾流分布特征。采用两台地基扫描激光雷达进行了风场实验,实验数据表明水平方向的近尾流分布类似于对称双高斯形,远尾流区域类似于对称高斯形,而垂直方向由于受到风切变的影响,在近尾流区域尾流分布类似非对称双高斯形,远尾流区域分布类似非对称高斯形。利用实测数据对三维尾流模型预测的水平剖面以及垂直剖面进行了对比验证,验证结果表明三维尾流模型的预测曲线和实验数据吻合良好,其平均相对误差大部分都在5%以内。新提出的三维尾流模型能够较好地预测风力机下游的整个尾流区域的空间分布,可为风电场的布局提供优化方案。Abstract: Aiming at the problem that the current wind turbine wake model can only describe the wake distribution in the far wake region and ignores the wake characteristics in the near wake region, this paper derives a new three-dimensional wake model based on the double-Gaussian function, using the flow conservation theorem and through rotation correction. The wake model considers the influence of wind shear and is able to describe the three-dimensional wake distribution characteristics in the near wake region and the far wake region. Wind field experiments were carried out with two ground-based scanning laser radars. The experimental data shows that the distribution of near wake in the horizontal direction has the symmetrical double-Gaussian shape, and the distribution of far wake area has the symmetrical Gaussian shape, while due to the influence of wind shear in the vertical direction, the distribution of wake in the near wake area has the asymmetrical double-Gaussian shape, and the distribution of far wake area has the asymmetrical Gaussian shape. The horizontal and vertical profiles predicted by the three-dimensional wake model are compared and verified by using the measured data. The validation results show that the prediction curves of the three-dimensional wake model are in good agreement with the experimental data, and the average relative errors are mostly within 5%. The newly proposed three-dimensional wake model can better predict the spatial distribution of the whole wake area downstream of the wind turbine and can provide an optimization scheme for the layout of the wind farm.
-
表 1 UP77风力机的参数规格
Table 1. Parameters and specifications of UP77 wind turbine
Categories Specifications Manufacturer United Power Model UP77 Rated power/ kW 1500 Rated wind speed/( m·s–1) 11.1 Diameter/m 77 Hub height/m 65 表 2 W3D6000的PPI模式和RHI模式设置参数
Table 2. PPI mode and RHI mode setting parameters of W3D6000
Scanning
modeAzimuth variation
range/ (°)Elevation variation
range/ (°)Scanning
time /minPPI 315~45 4,5,6,7,8,10,12,14,18,22,26,30,34,36,44 25 RHI 330~340 0~45 10 -
[1] 吴正人, 靳超然, 李非, 等. 风力机对大气边界层近地层影响的数值模拟[J]. 空气动力学学报, 2016, 34(6): 813-818.WU Z R, JIN C R, LI F, et al. Numerical simulation for influences of a wind turbine on surface-level of atmospheric boundary layer[J]. Acta Aerodynamica Sinica, 2016, 34(6): 813-818. (in Chinese) [2] 赵飞, 李兵兵, 蔚步超, 等. 考虑风切变的风电场尾流模型实验研究[J]. 中国测试, 2020, 46(1): 154-159. doi: 10.11857/j.issn.1674-5124.2019070025ZHAO F, LI B B, YU B C, et al. Experimental study on wake model of wind farm considering wind shear[J]. China Measurement & Test, 2020, 46(1): 154-159. (in Chinese) doi: 10.11857/j.issn.1674-5124.2019070025 [3] 田琳琳, 赵宁, 钟伟, 等. 风力机远尾流的计算研究[J]. 空气动力学学报, 2011, 29(6): 805-809, 814. doi: 10.3969/j.issn.0258-1825.2011.06.020TIAN L L, ZHAO N, ZHONG W, et al. Numerical analysis of the wind turbine’s far wake[J]. Acta Aerodynamica Sinica, 2011, 29(6): 805-809, 814. (in Chinese) doi: 10.3969/j.issn.0258-1825.2011.06.020 [4] SUN H Y, YANG H X. Study on an innovative three-dimensional wind turbine wake model[J]. Applied Energy, 2018, 226: 483-493. DOI: 10.1016/j.apenergy.2018.06.027 [5] LI L, HUANG Z, GE M W, et al. A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes[J]. Energy, 2022, 238: 121806. DOI: 10.1016/j.energy.2021.121806 [6] 吴正人, 张智博, 刘梅, 等. 基于OpenFOAM的层结性对风力机尾流大气参数的影响[J]. 可再生能源, 2022, 40(7): 926-931. doi: 10.3969/j.issn.1671-5292.2022.07.011WU Z R, ZHANG Z B, LIU M, et al. Influence of stratification properties on atmospheric parameters of wind turbine wake based on OpenFOAM[J]. Renewable Energy Resources, 2022, 40(7): 926-931. (in Chinese) doi: 10.3969/j.issn.1671-5292.2022.07.011 [7] 薛飞飞, 许昌, 黄海琴, 等. 基于格子玻尔兹曼方法的风力机尾流特性研究[J]. 中国电机工程学报, 2022, 42(12): 4352-4363.XUE F F, XU C, HUANG H Q, et al. Study on wake characteristics of wind turbine based on lattice boltzmann method[J]. Proceedings of the CSEE, 2022, 42(12): 4352-4363. (in Chinese) [8] 曹九发, 宋佺珉, 王超群, 等. 阵风工况下多台风力机尾流效应的非定常特性[J]. 空气动力学学报, 2022, 40(4): 247-255. doi: 10.7638/kqdlxxb-2021.0309CAO J F, SONG Q M, WANG C Q, et al. Unsteady characteristics of wake effect for multiple wind turbines under gust wind condition[J]. Acta Aerodynamica Sinica, 2022, 40(4): 247-255. (in Chinese) doi: 10.7638/kqdlxxb-2021.0309 [9] JENSEN N. A note on wind generator interaction[R]. Technical report from the Risø National (Laboratory Risø-M-2411), 1983. [10] BASTANKHAH M, PORTÉ-AGEL F. A new analytical model for wind-turbine wakes[J]. Renewable Energy, 2014, 70: 116-123. DOI: 10.1016/j.renene.2014.01.002 [11] FRANDSEN S, BARTHELMIE R, PRYOR S, et al. Analytical modelling of wind speed deficit in large offshore wind farms[J]. Wind Energy, 2006, 9(1-2): 39-53. DOI: 10.1002/we.189 [12] TIAN L L, ZHU W J, SHEN W Z, et al. Development and validation of a new two-dimensional wake model for wind turbine wakes[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 137: 90-99. DOI: 10.1016/j.jweia.2014.12.001 [13] GAO X X, LI B B, WANG T Y, et al. Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements[J]. Applied Energy, 2020, 260: 114272. DOI: 10.1016/j.apenergy.2019.114272 [14] BLONDEL F, CATHELAIN M. An alternative form of the super-Gaussian wind turbine wake model[J]. Wind Energy Science, 2020, 5(3): 1225-1236. DOI: 10.5194/wes-5-1225-2020 [15] VERMEER L J, SØRENSEN J N, CRESPO A. Wind turbine wake aerodynamics[J]. Progress in Aerospace Sciences, 2003, 39(6-7): 467-510. DOI: 10.1016/S0376-0421(03)00078-2 [16] KEANE A, AGUIRRE P E O, FERCHLAND H, et al. An analytical model for a full wind turbine wake[J]. Journal of Physics: Conference Series, 2016, 753: 032039. DOI: 10.1088/1742-6596/753/3/032039 [17] KEANE A. Advancement of an analytical double-Gaussian full wind turbine wake model[J]. Renewable Energy, 2021, 171: 687-708. DOI: 10.1016/j.renene.2021.02.078 [18] SCHREIBER J, BALBAA A, BOTTASSO C L. A double-Gaussian wake model [J]. Wind Energy, 2020, 5: 237-44. [19] IUNGO G V. Experimental characterization of wind turbine wakes: wind tunnel tests and wind LiDAR measurements[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2016, 149: 35-39. DOI: 10.1016/j.jweia.2015.11.009 [20] IUNGO G V, WU Y T, PORTÉ-AGEL F. Field measurements of wind turbine wakes with lidars[J]. Journal of Atmospheric and Oceanic Technology, 2013, 30(2): 274-287. DOI: 10.1175/jtech-d-12-00051.1 [21] MCTAVISH S, FESZTY D, NITZSCHE F. An experimental and computational assessment of blockage effects on wind turbine wake development[J]. Wind Energy, 2014, 17(10): 1515-1529. DOI: 10.1002/we.1648 [22] BODINI N, ZARDI D, LUNDQUIST J K. Three-dimensional structure of wind turbine wakes as measured by scanning lidar[J]. Atmospheric Measurement Techniques, 2017, 10(8): 2881-2896. DOI: 10.5194/amt-10-2881-2017 [23] KATIĆ I, JENSEN N O. A simple model for cluster efficiency[R] Proc. EWEC, 1, 1986. https://www.researchgate.net/publication/245066130_Article_A_simple_model_for_cluster_efficiency [24] GAO X X, ZHANG S H, LI L Q, et al. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test[J]. Energy, 2022, 254: 124277. DOI: 10.1016/j.energy.2022.124277 [25] HE R Y, YANG H X, SUN H Y, et al. A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes[J]. Applied Energy, 2021, 296: 117059. DOI: 10.1016/j.apenergy.2021.117059 -