场间尾流对风电场发电量的影响

张立栋; 李国浩; 杨世宇; 李钦伟; 潘东旭; 张煜涵; 陈国旗

doi:10.16513/j.2096-2185.DE.2308107

PDF(1815 KB)

分布式能源 ›› 2023, Vol. 8 ›› Issue (1) : 57-62. DOI: 10.16513/j.2096-2185.DE.2308107

应用技术

场间尾流对风电场发电量的影响

张立栋 ¹ ,
李国浩 ¹ ,
杨世宇 ¹ ,
李钦伟 ² ,
潘东旭 ³ ,
张煜涵 ⁴ ,
陈国旗 ⁵

作者信息 +

Influence of Wake Between Wind Farms on Wind Power Generation

Author information +

文章历史 +

摘要

风力机的主动偏航可以使尾流偏转，从而减小尾流对下游风力机的冲击。尾流效应会增大风力机的疲劳载荷和功率损失，上游风电场的尾流不断叠加，在风电场间形成大规模的尾迹簇，对下游风力机载荷和功率影响更加明显。为明确风电场间尾流的影响，使用WFsim仿真了偏航与未偏航时，上游风电场尾迹簇在场间和下游风电场的尾迹变化以及单个风力机的平均功率变化。以12 m/s为仿真风速，对2个相隔15D和20D的风电场进行了偏航和没有偏航的仿真分析，结果表明偏航可以大度幅提升下游风电场首排风力机的功率输出，并对之后的风力机影响相对较小，这意味着场间尾流主要影响下游风电场的首排风力机。另外，增大风电场间距可有效减小场间尾流影响。

Abstract

The active yaw of the wind turbine can deflect the wake, and thus, reduce the impact of the wake on the downstream wind turbine. The wake effect will increase the fatigue load and power loss of wind turbines. Furthermore, the wake of upstream wind farms is constantly superimposed, forming a large-scale wake cluster between wind farms, which has a more obvious influence on the load and power of downstream wind turbines. At present, there is a tendency for multiple wind farms to gather and develop in the same area. In order to clarify the influence of wake between wind farms, WFsim is used in this paper to simulate the changes of wake clusters between fields and in downstream wind farms as well as the changes of average power of a single wind turbine during yawed and unyawed. 12 m/s of wind speed is used in the simulation , the yawed and unyawed simulation analysis of two wind farms separated by 15D and 20D are carried out. The results show that yawed condition can increase the power output of the first row wind turbines of the downstream wind farm sharply, and has relatively little influence on the wind turbines behind the first row of the downstream wind farm, which means that the interfield wake mainly affects the first row wind turbines of the downstream wind farm. In addition, increasing the spacing between wind farms can reduce the impact of wake between fields effectively.

导出引用

张立栋, 李国浩, 杨世宇, 等. 场间尾流对风电场发电量的影响[J]. 分布式能源. 2023, 8(1): 57-62 https://doi.org/10.16513/j.2096-2185.DE.2308107

Lidong ZHANG, Guohao LI, Shiyu YANG, et al. Influence of Wake Between Wind Farms on Wind Power Generation[J]. Distributed Energy Resources. 2023, 8(1): 57-62 https://doi.org/10.16513/j.2096-2185.DE.2308107

中图分类号： TK81

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	徐彬，薛帅，高厚磊，等. 海上风电场及其关键技术发展现状与趋势[J]. 发电技术，2022, 43(2): 227-235. XU Bin, XUE Shuai, GAO Houlei, et al. Development status and prospects of offshore wind farms and it's key technology[J]. Power Generation Technology, 2022, 43(2): 227-235. 本文引用 [1]

[2]	国家发展改革委. 可再生能源发展“十三五”规划[EB/OL]. (2017-05-16) [2021-01-20]. 本文引用 [1]

[3]	SADORSKY P. Wind energy for sustainable development: Driving factors and future outlook[J]. Journal of Cleaner Production, 2021, 289: 125779. 本文引用 [1]

[4]	于龙泽，肖白，孙立国. 风光出力场景生成的条件深度卷积生成对抗网络方法[J]. 东北电力大学学报，2021, 41(6): 90-99. YU Longze, XIAO Bai, SUN Liguo. Conditional depth convolution generation of confrontation network method for scenery output scenario generation[I]. Journal of Northeast Electric Power University, 2021, 41(6): 90-99. 本文引用 [1]

[5]

姜红丽，刘羽茜，冯一铭，等. 碳达峰、碳中和背景下“十四五”时期发电技术趋势分析[J]. 发电技术，2022, 43(1): 54-64.

JIANG

Hongli

, LIU

Yuxi

, FENG

Yiming

, et al. Analysis of power generation technology trend in 14th Five-Year Plan under the background of carbon peak and carbon neutrality[J]. Power Generation Technology, 2022, 43(1): 54-64.

本文引用 [1]

[6]	周强，马彦宏，沈琛云，等. 新时期中国西北地区新能源可持续发展反思与建议[J]. 电网与清洁能源，2020, 36(6): 78-84. ZHOU Qiang, MA Yanhong, SHEN Chenyun, et al. Reflection and suggestions on sustainable development of new energy in northwest china in new era[J]. Power System and Clean Energy, 2020, 36(6): 78-84. 本文引用 [1]

[7]	杨茂，代博祉，刘蕾. 风电功率概率预测研究综述[J]. 东北电力大学学报，2020, 40(2): 1-6. YANG Mao, DAI Bozhi, LIU Lei. Review of probabilistic prediction of wind power[J]. Journal of Northeast Electric Power University, 2020, 40(2): 1-6. 本文引用 [1]

[8]	杨茂，于欣楠. 考虑风电场功率爬坡的超短期组合预测[J]. 东北电力大学学报，2022, 42(1): 63-70. YANG Mao, Yu Xinnan. Ultra-short term combined prediction considering power climb of wind farm[J]. Journal of Northeast Electric Power University, 2022, 42(1): 63-70. 本文引用 [1]

[9]	GONZÁLEZ-LONGATT F, WALL P, TERZIJA V. Wake effect in wind farm performance: Steady-state and dynamic behavior[J]. Renewable Energy, 2012, 39(1): 329-338. 本文引用 [1]

[10]	NYGAARD, N. G, HANSEN, S. D. Wake effects between two neighbouring wind farms[J]. Journal of Physics: Conference Series. IOP Publishing, 2016, 753(3), 032020. 本文引用 [2]

[11]	阎洁，张永蕊，张浩. 区域风电场群集中式功率预测系统设计与应用[J]. 分布式能源，2022, 7(1): 28-36. YAN Jie, ZHANG Yongrui, ZHANG Hao. Design and application of centralized power forecasting system for regional wind farm cluster[J]. Distributed Energy, 2022, 7(1): 28-36. 本文引用 [1]

[12]	刘栋，魏霞，王维庆，等. 基于SSA-ELM的短期风电功率预测[J]. 智慧电力，2021, 49(6): 53-59. LIU Dong, WEI Xia, WANG Weiqing, et al. Short-term wind power prediction based on SSA-ELM[J]. Smart Power, 2021, 49(6): 53-59. 本文引用 [1]

[13]	郑俊观，王硕禾，张立园. 考虑尾流效应的并网风电场等值建模与仿真[J]. 分布式能源，2017, 2(4): 7-12. ZHENG Junguan, WANG Suohe, ZHANG Liyuan. Equivalent modeling and simulation of grid-connected wind farm considering wake effect[J]. Distributed Energy, 2017, 2(4): 7-12. 本文引用 [1]

[14]	KIRANOUDIS C T, MAROULIS Z B. Effective short-cut modelling of wind park efficiency[J]. Renewable Energy, 1997, 11(4): 439-457. 本文引用 [1]

[15]	KEANE A, AGUIRRE P E O, FERCHLAND H, et al. An analytical model for a full wind turbine wake[C]//Journal of Physics: Conference Series. IOP Publishing, 2016, 753(3)：032039. 本文引用 [1]

[16]	KEANE A. Advancement of an analytical double-Gaussian full wind turbine wake model[J]. Renewable Energy, 2021, 171: 687-708. 本文引用 [1]

[17]	褚景春，袁凌，李方敏，等. 基于致动盘模型的单台风电机组尾流流场模拟[J]. 分布式能源，2018, 3(3): 10-14. CHU Jingchun, YUAN Ling, LI Fangmin, et al. Numerical simulation of wind turbine wake field based on actuator disc model[J]. Distributed Energy, 2018, 3(3): 10-14. 本文引用 [1]

[18]	GE M, WU Y, LIU Y, et al. A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes[J]. Applied Energy, 2019, 233: 975-984. 本文引用 [1]

[19]	孙涛，段琦玮，党群，等. 风力机尾迹冲击在线检测算法[J]. 分布式能源，2021, 6(1): 51-55. SUN Tao, DUAN Qiwei, DANG Qun, et al. On-line detection algorithm of wind turbine wake impact[J]. Distributed Energy, 2021, 6(1): 51-55. 本文引用 [1]

[20]	孙涛. 场间尾迹簇对风电场发电量和载荷的影响分析[J]. 分布式能源，2021, 6(2): 56-60. SUN Tao. Analysis of influence of cluster wakes between wind farms on wind power generation and load[J]. Distributed Energy, 2021, 6(2): 56-60. 本文引用 [1]

[21]	张磊，李继影，李钦伟，等. 风力机偏航系统控制策略研究现状及进展[J]. 发电技术，2021, 42(3): 306-312. ZHANG Lei, LI Jiying, LI Qinwei, et al. Status and prospect of yaw system control strategy for wind turbines[J]. Power Generation Technology, 2021, 42(3): 306-312. 本文引用 [1]

[22]

郭茂丰，张立茹，李得银，等. 偏航状态下水平轴风力机尾迹偏移及湍流特征分析[J]. 排灌机械工程学报，2020, 38(7): 702-707.

GUO

Maofeng

, ZHANG

Liru

, LI

Deyin

, et al. Analysis on wake deviation and turbulence characteristics of horizontal-axis wind turbine under yawed condition[J]. Journal of Drainage and Irrigation Machinery Engineering, 2020, 38(7): 702-707.

本文引用 [1]

[23]	BOERSMA S, DOEKEMEIJER B, VALI M, et al. A control-oriented dynamic wind farm model: WFSim[J]. Wind Energy Science, 2018, 3(1): 75-95. 本文引用 [1]

[24]	CHOI D, SHIN W, KO K, et al. Static and dynamic yaw misalignments of wind turbines and machine learning based correction methods using lidar data[J]. IEEE Transactions on Sustainable Energy. 2018, 10(2), 971-982 本文引用 [1]

[25]	JONKMAN J, BUTTERFIELD S, MUSIAL W, et al. Definition of a 5-MW reference wind turbine for offshore system development[R]. National Renewable Energy Lab. (NREL), Golden, CO (United States), 2009. 本文引用 [1]