1. 华北电力大学 动力工程系,河北,保定,071003
2. 河北省低碳高效发电技术重点实验室,河北,保定,071003
3. 保定市低碳高效发电技术重点实验室,河北,保定,071003
4. 华北电力大学 电子与通信工程系,河北,保定,071003
5. 华北电力大学 电气与电子工程学院,河北,保定,071003
[ "高晓霞(1985—),女,河北石家庄人,副教授,博士,研究方向为风力发电,E-mail:okspringgao@hotmail.com" ]
网络出版:2025-04-28,
纸质出版:2025
移动端阅览
高晓霞,吕陶,马万里,朱霄珣,王瑜,赵飞. 风力机偏航三维动态尾流特性及现场实验验证动力工程学报, 2025, 45(4): 537-543 https://doi.
org/10.19805/j.cnki.jcspe.2025.240015
高晓霞,吕陶,马万里,朱霄珣,王瑜,赵飞. 风力机偏航三维动态尾流特性及现场实验验证动力工程学报, 2025, 45(4): 537-543 https://doi. DOI: 10.19805/j.cnki.jcspe.2025.240015.
org/10.19805/j.cnki.jcspe.2025.240015 DOI:
对三维偏航全尾流模型进行了改进
考虑偏航风力机尾流的动态时延特性并进一步考虑其对尾流场空间分布的影响
提出一种风力机偏航三维动态全尾流模型
即Y-3DJGF-T模型。在某陆上风电场使用2台垂直式激光雷达进行现场实验
并结合数据采集与监视控制(SCADA)系统数据分析了偏航状态下尾流延迟时间及速度变化
以验证Y-3DJGF-T模型在尾流中心线和垂直剖面风速的预测准确性。结果表明:Y-3DJGF-T模型计算值与实测值的平均相对误差为2.51%。
The three-dimensional yaw entire wake model was improved
and a three-dimensional dynamic entire wake model of wind turbine yaw
namely the Y-3DJGF-T model
was proposed
considering the dynamic delay characteristics of the yaw wind turbine wake and further considering its influence on the spatial distribution of the wake field. To verify the accuracy of the Y-3DJGF-T model in wake centerline and vertical profile
two vertical lidars were used to conduct field experiments
and the data acquisition and supervisory control (SCADA) system data were used to analyze the wake delay time and velocity changes under yaw conditions. Results show that the average relative error between the calculated and measured values of the Y-3DJGF-T model is 2.51%.
胡丹梅, 霍能萌, 杨官奎, 等. 风向变化对风力机尾流影响的数值分析[J]. 动力工程学报, 2017, 37(1): 60-65. HU Danmei, HUO Nengmeng, YANG Guankui, et al. Numerical analysis on wake effect of wind turbines at varying wind directions[J]. Journal of Chinese Society of Power Engineering, 2017, 37(1): 60-65.
LOPES A M G, VICENTE A H S N, SNCHEZ O H, et al. Operation assessment of analytical wind turbine wake models[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 220: 104840.
DUAN Guiyue, DAR A S, PORT-AGEL F. A wind tunnel study on cyclic yaw control: power performance and wake characteristics[J]. Energy Conversion and Management, 2023, 293: 117445.
张绍海, 高晓霞, 徐施耐, 等. 基于风场实验的三维叠加尾流模型特性研究[J]. 动力工程学报, 2023, 43(9): 1223-1229. ZHANG Shaohai, GAO Xiaoxia, XU Shinai, et al. Research on characteristics of three-dimensional superimposed wake model based on wind field experiments[J]. Journal of Chinese Society of Power Engineering, 2023, 43(9): 1223-1229.
王天凡, 施鎏鎏. 来流剪切对风力机尾迹影响的数值研究[J]. 动力工程学报, 2021, 41(10): 877-882, 891. WANG Tianfan, SHI Liuliu. Numerical study on the influence of shear inflow on wind turbine wakes[J]. Journal of Chinese Society of Power Engineering, 2021, 41(10): 877-882, 891.
STOREY R C, CATER J E, NORRIS S E. Large eddy simulation of turbine loading and performance in a wind farm[J]. Renewable Energy, 2016, 95: 31-42.
GAO Zhiteng, LI Ye, WANG Tongguang, et al. Modelling the nacelle wake of a horizontal-axis wind turbine under different yaw conditions[J]. Renewable Energy, 2021, 172: 263-275.
ARABGOLARCHEH A, JANNESARAHMADI S, BENINI E. Modeling of near wake characteristics in floating offshore wind turbines using an actuator line method[J]. Renewable Energy, 2022, 185: 871-887.
JIMNEZ , CRESPO A, MIGOYA E. Application of a LES technique to characterize the wake deflection of a wind turbine in yaw[J]. Wind Energy, 2010, 13(6): 559-572.
BASTANKHAH M, PORT-AGEL F. Experimental and theoretical study of wind turbine wakes in yawed conditions[J]. Journal of Fluid Mechanics, 2016, 806: 506-541.
ZHU Xiaoxun, CHEN Yao, XU Shinai, et al. Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification[J]. Energy, 2023, 270: 126907.
DOU Bingzheng, GUALA M, LEI Liping, et al. Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel[J]. Energy, 2019, 166: 819-833.
HE Ruiyang, YANG Hongxing, SUN Haiying, et al. A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes[J]. Applied Energy, 2021, 296: 117059.
CHEN Hao, STAUPE-DELGADO R. Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed[J]. Energy Reports, 2022, 8: 864-870.
XU Li, ZHOU Guanhao, GUO Zhaoliang. ART-LSTANet: an adaptive intelligent method for wind turbine wake analysis[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 106809.
GEBRAAD P M O, VAN WINGERDEN J W. A control-oriented dynamic model for wakes in wind plants[J]. Journal of Physics: Conference Series, 2014, 524: 012186.
KHEIRABADI A C, NAGAMUNE R. A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion[J]. Ocean Engineering, 2021, 234: 109313.
DENG Zhiwen, XU Chang, HUO Zhihong, et al. Yaw optimisation for wind farm production maximisation based on a dynamic wake model[J]. Energies, 2023, 16(9): 3932.
GAO Xiaoxia, ZHANG Shaohai, LI Luqing, et al. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test[J]. Energy, 2022, 254: 124277.
0
浏览量
124
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621