水平轴风力机主动尾流控制综述

宗豪华; 孙恩博

doi:10.7638/kqdlxxb-2021.0249

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

水平轴风力机主动尾流控制综述

综述 | 更新时间：2026-05-28

- 水平轴风力机主动尾流控制综述
- Reivew of active wake control for horizontal-axis wind turbines
- 空气动力学学报 2022年40卷第4期页码：51-68
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
  
  宗豪华*（1992-），男，河南汝州人，讲师，研究方向：等离子体流动控制，风力机主动尾流控制. E-mail：haohua_zong@163.com
- 基金信息：
  
  国家自然科学基金青年科学基金项目（12002384）
- DOI：10.7638/kqdlxxb-2021.0249
  中图分类号： TM315
- 纸质出版：2022
- 稿件说明：
移动端阅览
宗豪华, 孙恩博. 水平轴风力机主动尾流控制综述[J]. 空气动力学学报, 2022, 40(4): 51−68.

ZONG Haohua, SUN Enbo. Reivew of active wake control for horizontal-axis wind turbines[J]. 2022, 40(4): 51-68.
宗豪华, 孙恩博. 水平轴风力机主动尾流控制综述[J]. 空气动力学学报, 2022, 40(4): 51−68. DOI： 10.7638/kqdlxxb-2021.0249

ZONG Haohua, SUN Enbo. Reivew of active wake control for horizontal-axis wind turbines[J]. 2022, 40(4): 51-68. DOI： 10.7638/kqdlxxb-2021.0249.

摘要

对于大型风电场而言，由尾流干扰引起的产能损失最高可达30%～40%。主动尾流控制（AWC）技术通过上游风力机的偏航来诱导尾流侧向偏转，以减弱对后排风力机的尾流干扰、增加风电场的总产能，应用前景极其广阔。本文从无偏航风力机尾流模型、偏航风力机尾流模型、多风力机尾流叠加方法和风电场产能优化四个角度，综述了AWC技术在过去近20年的研究进展，并对该技术走向工程应用中所亟需解决的问题进行了展望。整体来看，该项技术已经基本成熟。研究人员从最初的理论、仿真和风洞实验研究，走向了实际风电场的效果验证。在理论预测层面，高斯尾流亏损模型、基于旋涡诱导横向速度的一次和二次尾流偏转模型、具备动量守恒特性的尾流叠加方法正在成为风电场产能预测的标配。实际工程应用层面，风力机间距、排数、湍流度、大气边界层热稳定性、风向和风速变化均会影响AWC所带来的产能收益；典型条件下，应用AWC技术后，全尾流干扰风向上的风电场产能可提高约5%～15%，各个风向平均后的年均收益约为1%～3%。

Abstract

Large-scale wind farms with multiple rows of horizontal-axis wind turbines suffer from significant power losses (30%-40%) due to wake interactions. To deal with this situation

the yaw-based active wake control (AWC) has been proposed. The principle of AWC is to yaw the upstream wind turbines so that the wake can be deflected away from the turbine row

which hopefully will lead to a net gain of the total wind farm power production. In this paper

the progress of the AWC technique in the past decade is reviewed from four aspects: wake models of single non-yawed wind turbine

wake models of single yawed wind turbine

wake superposition methods for multiple wind turbines

and wind farm power optimization. Meanwhile

issues needed to be addressed before being applied to engineering are summarized. Based on these reviews

it is fair to conclude that the AWC technique is more or less mature now

in the sense that earlier laboratory results from analytical modeling

numerical simulations

and wind tunnel studies have been successfully applied to field tests of commercial wind farms

and significant improvement of the net power gain has been obtained. In terms of theoretical progress

EPFL Gaussian wake models

primary and secondary wake deflection models based on the vortex-induced cross-wind velocities

momentum-conserving wake superposition laws are increasingly becoming the standard in the wind farm power prediction. Regarding practical engineering

it has been found that a whole bunch of parameters such as turbine rows

streamwise turbine spacing

turbulence intensity

thermal instability of atmospheric boundary layer

wind speed

and direction variability can affect the magnitude of net power gain in the active wake control. According to recent field tests performed by National Renewable Energy Laboratory (NREL

US) and Stanford University

AWC is able to improve the total wind farm power production by 5%-15% if the wind direction is aligned with turbine rows

and when these net power gains are averaged over all wind directions

an increase of 1%-3% in the wind farm efficiency is expected.

关键词

Keywords

references

PORTÉ-AGEL F, BASTANKHAH M, SHAMSODDIN S. Wind-turbine and wind-farm flows: a review[J]. Boundary-Layer Meteorology, 2020, 174(1): 1-59. DOI:10.1007/s10546-019-00473-0

LI Z W, HE D X. Reviews of fluid dynamics researches in wind energy engineering[J]. Advances in Mechanics, 2013, 43(5): 472-525. (in Chinese)

STEVENS R J A M, MENEVEAU C. Flow structure and turbulence in wind farms[J]. Annual Review of Fluid Mechanics, 2017, 49(1): 311-339. DOI:10.1146/annurev-fluid-010816-060206

BARTHELMIE R J, HANSEN K, FRANDSEN S T, et al. Modelling and measuring flow and wind turbine wakes in large wind farms offshore[J]. Wind Energy, 2009, 12(5): 431-444. DOI:10.1002/we.348

BARTHELMIE R J, PRYOR S C, FRANDSEN S T, et al. Quantifying the impact of wind turbine wakes on power output at offshore wind farms[J]. Journal of Atmospheric and Oceanic Technology, 2010, 27(8): 1302-1317. DOI:10.1175/2010jtecha1398.1

BARTHELMIE R J, JENSEN L E. Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm[J]. Wind Energy, 2010, 13(6): 573-586. DOI:10.1002/we.408

HASAGER C, RASMUSSEN L, PEÑA A, et al. Wind farm wake: the horns rev photo case[J]. Energies, 2013, 6(2): 696-716. DOI:10.3390/en6020696

MEDICI D. Experimental studies of wind turbine wakes: power optimisation and meandering[D]. KTH, 2005.

ADARAMOLA M S, KROGSTAD P Å. Experimental investigation of wake effects on wind turbine performance[J]. Renewable Energy, 2011, 36(8): 2078-2086. DOI:10.1016/j.renene.2011.01.024

WAGENAAR J W, MACHIELSE L A H, SCHEPERS J G. Controlling wind in ECN’s scaled wind farm[C]// EWEA 2012. https://www.researchgate.net/publication/264851319_Controlling_Wind_in_ECN's_Scaled_Wind_Farm

DAR Z, KAR K, SAHNI O, et al. Windfarm power optimization using yaw angle control[J]. IEEE Transactions on Sustainable Energy, 2017, 8(1): 104-116. DOI:10.1109/TSTE.2016.2585883

WANG J, BOTTASSO C L, CAMPAGNOLO F. Wake redirection: comparison of analytical, numerical and experimental models[C]//Journal of Physics: Conference Series. IOP Publishing, 2016, 753(3): 032064. https://re.public.polimi.it/retrieve/handle/11311/1007400/163974/WANGJ01-16.pdf doi: 10.1088/1742-6596/753/3/032064

CAMPAGNOLO F, PETROVIĆ V, BOTTASSO C L, et al. Wind tunnel testing of wake control strategies[C]//2016 American Control Conference (ACC), Boston, MA, USA. IEEE, 2016: 513-518. doi: 10.1109/ACC.2016.7524965

HOWLAND M F, LELE S K, DABIRI J O. Wind farm power optimization through wake steering[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(29): 14495-14500. https://www.pnas.org/content/pnas/116/29/14495.full.pdf doi: 10.1073/pnas.1903680116

BASTANKHAH M, PORTÉ-AGEL F. Wind farm power optimization via yaw angle control: a wind tunnel study[J]. Journal of Renewable and Sustainable Energy, 2019, 11(2): 023301. DOI:10.1063/1.5077038

ZONG H H, PORTÉ-AGEL F. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization[J]. Renewable Energy, 2021, 170: 1228-1244. DOI:10.1016/j.renene.2021.02.059

NASH R, NOURI R, VASEL-BE-HAGH A. Wind turbine wake control strategies: a review and concept proposal[J]. Energy Conversion and Management, 2021, 245: 114581. DOI:10.1016/j.enconman.2021.114581

NANOS E M, BOTTASSO C L, MANOLAS D I, et al. Vertical wake deflection for floating wind turbines by differential ballast control[J]. Wind Energy Science Discussions, 2021(Accepted). https://www.researchgate.net/publication/354010920_Vertical_wake_deflection_for_floating_wind_turbines_by_differential_ballast_control doi: 10.5194/wes-2021-79

NANOS E M, LETIZIA S, CLEMENTE D J B, et al. Vertical wake deflection for offshore floating wind turbines by differential ballast control[J]. Journal of Physics: Conference Series, 2020, 1618: 022047. DOI:10.1088/1742-6596/1618/2/022047

KIMURA K, TANABE Y, MATSUO Y, et al. Forced wake meandering for rapid recovery of velocity deficits in a wind turbine wake[C]//AIAA Scitech 2019 Forum, San Diego, California. Reston, Virginia: AIAA, 2019: 2083. doi: 10.2514/6.2019-2083

WANG J, FOLEY S, NANOS E M, et al. Numerical and experimental study of wake redirection techniques in a boundary layer wind tunnel[J]. Journal of Physics: Conference Series, 2017, 854: 012048. DOI:10.1088/1742-6596/854/1/012048

FLEMING P A, GEBRAAD P M O, LEE S, et al. Evaluating techniques for redirecting turbine wakes using SOWFA[J]. Renewable Energy, 2014, 70: 211-218. DOI:10.1016/j.renene.2014.02.015

FLEMING P, GEBRAAD P M O, LEE S, et al. Simulation comparison of wake mitigation control strategies for a two-turbine case[J]. Wind Energy, 2015, 18(12): 2135-2143. DOI:10.1002/we.1810

FLEMING P, ANNONI J, SHAH J J, et al. Field test of wake steering at an offshore wind farm[J]. Wind Energy Science, 2017, 2(1): 229-239. DOI:10.5194/wes-2-229-2017

FLEMING P A, NING A, GEBRAAD P M O, et al. Wind plant system engineering through optimization of layout and yaw control[J]. Wind Energy, 2016, 19(2): 329-344.https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/we.1836 DOI: 10.1002/we.1836

BENSASON D, SIMLEY E, ROBERTS O, et al. Evaluation of the potential for wake steering for US land-based wind power plants[J]. Journal of Renewable and Sustainable Energy, 2021, 13(3): 033303. DOI:10.1063/5.0039325

SONG D R, YANG J, FAN X Y, et al. Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions[J]. Energy Conversion and Management, 2018, 157: 587-599. DOI:10.1016/j.enconman.2017.12.019

SØRENSEN J N. Aerodynamic aspects of wind energy conversion[J]. Annual Review of Fluid Mechanics, 2011, 43(1): 427-448. DOI:10.1146/annurev-fluid-122109-160801

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

MEDICI D, IVANELL S, DAHLBERG J Å, et al. The upstream flow of a wind turbine: blockage effect[J]. Wind Energy, 2011, 14(5): 691-697. DOI:10.1002/we.451

BASTANKHAH M, PORTÉ-AGEL F. Wind tunnel study of the wind turbine interaction with a boundary-layer flow: Upwind region, turbine performance, and wake region[J]. Physics of Fluids, 2017, 29(6): 065105. DOI:10.1063/1.4984078

DASARI T, WU Y, LIU Y, et al. Near-wake behaviour of a utility-scale wind turbine[J]. Journal of Fluid Mechanics, 2019, 859: 204-246. DOI:10.1017/jfm.2018.779

HONG J R, TOLOUI M, CHAMORRO L P, et al. Natural snowfall reveals large-scale flow structures in the wake of a 2.5-MW wind turbine[J]. Nature Communications, 2014, 5: 4216. DOI:10.1038/ncomms5216

CHAMORRO L P, PORTÉ-AGEL F. Effects of thermal stability and incoming boundary-layer flow characteristics on wind-turbine wakes: a wind-tunnel study[J]. Boundary-Layer Meteorology, 2010, 136(3): 515-533. DOI:10.1007/s10546-010-9512-1

POPE S B. Turbulent flows[J]. Measurement Science and Technology, 2001, 12(11). DOI:10.1088/0957-0233/12/11/705

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

AINSLIE J F. Calculating the flowfield in the wake of wind turbines[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1988, 27(1-3): 213-224. DOI:10.1016/0167-6105(88)90037-2

FANG J N, PORTÉ-AGEL F. Large-eddy simulation of very-large-scale motions in the neutrally stratified atmospheric boundary layer[J]. Boundary-Layer Meteorology, 2015, 155(3): 397-416. DOI:10.1007/s10546-015-0006-z

LARSEN T J, MADSEN H A, LARSEN G C, et al. Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm[J]. Wind Energy, 2013, 16(4): 605-624. DOI:10.1002/we.1563

ESPAÑA G, AUBRUN S, LOYER S, et al. Wind tunnel study of the wake meandering downstream of a modelled wind turbine as an effect of large scale turbulent eddies[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2012, 101: 24-33. DOI:10.1016/j.jweia.2011.10.011

WU Y T, PORTÉ-AGEL F. Large-eddy simulation of wind-turbine wakes: evaluation of turbine parametrisations[J]. Boundary-Layer Meteorology, 2011, 138(3): 345-366. DOI:10.1007/s10546-010-9569-x

JENSEN N O. A note on wind turbine interaction[R ] . Riso-M-2411. Risoe National Laboratory, Roskilde, Denmark, 1983: 16. https://backend.orbit.dtu.dk/ws/portalfiles/portal/55857682/ris_m_2411.pdf

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.https://onlinelibrary.wiley.com/doi/epdf/10.1002/we.189 DOI:10.1002/we.189

HANSEN M. Aerodynamics of wind turbines[M]. Routledge, 2015. DOI:10.4324/9781315769981

SHAMSODDIN S, PORTÉ-AGEL F. A model for the effect of pressure gradient on turbulent axisymmetric wakes[J]. Journal of Fluid Mechanics, 2018, 837: R3. DOI:10.1017/jfm.2017.864

ABKAR M, SØRENSEN J, PORTÉ-AGEL F. An analytical model for the effect of vertical wind veer on wind turbine wakes[J]. Energies, 2018, 11(7): 1838. DOI:10.3390/en11071838

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

ARCHER C L, VASEL-BE-HAGH A, YAN C, et al. Review and evaluation of wake loss models for wind energy applications[J]. Applied Energy, 2018, 226: 1187-1207. DOI:10.1016/j.apenergy.2018.05.085

FLEMING P A, SCHOLBROCK A K, JEHU A, et al. Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment[J]. Journal of Physics: Conference Series, 2014, 524: 012002. DOI:10.1088/1742-6596/524/1/012002

ZONG H H, PORTÉ-AGEL F. A point vortex transportation model for yawed wind turbine wakes[J]. Journal of Fluid Mechanics, 2020, 890: A8. DOI:10.1017/jfm.2020.123

BASTANKHAH M, PORTÉ-AGEL F. Experimental and theoretical study of wind turbine wakes in yawed conditions[J]. Journal of Fluid Mechanics, 2016, 806: 506-541. DOI:10.1017/jfm.2016.595

HOWLAND M F, BOSSUYT J, MARTÍNEZ-TOSSAS L A, et al. Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions[J]. Journal of Renewable and Sustainable Energy, 2016, 8(4): 043301. DOI:10.1063/1.4955091

MARTÍNEZ-TOSSAS L A, ANNONI J, FLEMING P A, et al. The aerodynamics of the curled wake: a simplified model in view of flow control[J]. Wind Energy Science, 2019, 4(1): 127-138. DOI:10.5194/wes-4-127-2019

MARTÍNEZ-TOSSAS L A, KING J, QUON E, et al. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows[J]. Wind Energy Science, 2021, 6(2): 555-570. DOI:10.5194/wes-6-555-2021

JIMÉNEZ Á, 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. DOI:10.1002/we.380

SHAPIRO C R, GAYME D F, MENEVEAU C. Modelling yawed wind turbine wakes: a lifting line approach[J]. Journal of Fluid Mechanics, 2018, 841: R1. DOI:10.1017/jfm.2018.75

QIAN G W, ISHIHARA T. A new analytical wake model for yawed wind turbines[J]. Energies, 2018, 11(3): 665. DOI:10.3390/en11030665

BRUGGER P, DEBNATH M, SCHOLBROCK A, et al. Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models[J]. Wind Energy Science, 2020, 5(4): 1253-1272. DOI:10.5194/wes-5-1253-2020

BRANLARD E, GAUNAA M. Cylindrical vortex wake model: skewed cylinder, application to yawed or tilted rotors[J]. Wind Energy, 2016, 19(2): 345-358. DOI:10.1002/we.1838[LinkOut]

VOLLMER L, STEINFELD G, HEINEMANN D, et al. Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study[J]. Wind Energy Science, 2016, 1(2): 129-141. DOI:10.5194/wes-1-129-2016

LISSAMAN P B S. Energy effectiveness of arbitrary arrays of wind turbines[J]. Journal of Energy, 1979, 3(6): 323-328. DOI:10.2514/3.62441

KATIC I, HØJSTRUP J, JENSEN N O. A simple model for cluster efficiency[C]//European wind energy association conference and exhibition. 1986, 1: 407-410. https://backend.orbit.dtu.dk/ws/portalfiles/portal/106427419/A_Simple_Model_for_Cluster_Efficiency_EWEC_86_.pdf

NIAYIFAR A, PORTÉ-AGEL F. Analytical modeling of wind farms: a new approach for power prediction[J]. Energies, 2016, 9(9): 741. DOI:10.3390/en9090741

VOUTSINAS S, RADOS K, ZERVOS A. On the analysis of wake effects in wind parks[J]. Wind Engineering, 1990, 14(4): 204-219. https://www.jstor.org/stable/43749429

ZONG H H, PORTÉ-AGEL F. A momentum-conserving wake superposition method for wind farm power prediction[J]. Journal of Fluid Mechanics, 2020, 889: A8. DOI:10.1017/jfm.2020.77

BASTANKHAH M, PORTÉ-AGEL F. A new miniature wind turbine for wind tunnel experiments. part I: design and performance[J]. Energies, 2017, 10(7): 908. DOI:10.3390/en10070908

FLEMING P, ANNONI J, CHURCHFIELD M, et al. A simulation study demonstrating the importance of large-scale trailing vortices in wake steering[J]. Wind Energy Science, 2018, 3(1): 243-255. DOI:10.5194/wes-3-243-2018

FLEMING P, KING J, DYKES K, et al. Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1[J]. Wind Energy Science, 2019, 4(2): 273-285. DOI:10.5194/wes-4-273-2019

FLEMING P, KING J, SIMLEY E, et al. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2[J]. Wind Energy Science, 2020, 5(3): 945-958. DOI:10.5194/wes-5-945-2020

KING J, FLEMING P, KING R, et al. Control-oriented model for secondary effects of wake steering[J]. Wind Energy Science, 2021, 6(3): 701-714. DOI:10.5194/wes-6-701-2021

KNUDSEN T, BAK T, SVENSTRUP M. Survey of wind farm control—power and fatigue optimization[J]. Wind Energy, 2015, 18(8): 1333-1351. DOI:10.1002/we.1760

PARK J, LAW K H. A data-driven, cooperative wind farm control to maximize the total power production[J]. Applied Energy, 2016, 165: 151-165. DOI:10.1016/j.apenergy.2015.11.064

KIRCHNER-BOSSI N, PORTÉ-AGEL F. Wind farm area shape optimization using newly developed multi-objective evolutionary algorithms[J]. Energies, 2021, 14(14): 4185. DOI:10.3390/en14144185

GEBRAAD P M O, VAN WINGERDEN J W. Maximum power‐point tracking control for wind farms[J]. Wind Energy, 2015, 18(3): 429-447. DOI: 10.1002/we.1706

GEBRAAD P, THOMAS J J, NING A, et al. Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control[J]. Wind Energy, 2017, 20(1): 97-107. DOI:10.1002/we.1993

BROGNA R, FENG J, SØRENSEN J N, et al. A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain[J]. Applied Energy, 2020, 259: 114189. DOI:10.1016/j.apenergy.2019.114189

KIRCHNER-BOSSI N, PORTÉ-AGEL F. Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model[J]. Energies, 2018, 11(12): 3268. DOI:10.3390/en11123268

STANFEL P, JOHNSON K, BAY C J, et al. Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind[J]. Journal of Renewable and Sustainable Energy, 2021, 13(4): 043305. DOI:10.1063/5.0043091

SONG D R, FAN X Y, YANG J, et al. Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method[J]. Applied Energy, 2018, 224: 267-279. DOI:10.1016/j.apenergy.2018.04.114

BERNARDONI F, CIRI U, ROTEA M A, et al. Identification of wind turbine clusters for effective real time yaw control optimization[J]. Journal of Renewable and Sustainable Energy, 2021, 13(4): 043301. DOI:10.1063/5.0036640

OZBAY A, TIAN W, YANG Z F, et al. Interference of wind turbines with different yaw angles of the upstream wind turbine[C]//42nd AIAA Fluid Dynamics Conference and Exhibit, New Orleans, Louisiana. Reston, Virginia: AIAA, 2012. doi: 10.2514/6.2012-2719

HANSEN K S, BARTHELMIE R J, JENSEN L E, et al. The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm[J]. Wind Energy, 2012, 15(1): 183-196. DOI:10.1002/we.512

CAMPAGNOLO F, PETROVIĆ V, SCHREIBER J, et al. Wind tunnel testing of a closed-loop wake deflection controller for wind farm power maximization[J]. Journal of Physics: Conference Series, 2016, 753: 032006. https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032006/pdf doi: 10.1088/1742-6596/753/3/032006

LIN M, PORTÉ-AGEL F. Power maximization and fatigue-load mitigation in a wind-turbine array by active yaw control: an LES study[J]. Journal of Physics: Conference Series, 2020, 1618: 042036. DOI:10.1088/1742-6596/1618/4/042036

FLEMING P, ANNONI J, SCHOLBROCK A, et al. Full-scale field test of wake steering[J]. Journal of Physics: Conference Series, 2017, 854: 012013. DOI:10.1088/1742-6596/854/1/012013

GEBRAAD P M O, TEEUWISSE F W, VAN WINGERDEN J W, et al. Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study[J]. Wind Energy, 2016, 19(1): 95-114. DOI:10.1002/we.1822

ROTT A, DOEKEMEIJER B, SEIFERT J K, et al. Robust active wake control in consideration of wind direction variability and uncertainty[J]. Wind Energy Science, 2018, 3(2): 869-882. DOI:10.5194/wes-3-869-2018

MENDEZ REYES H, KANEV S, DOEKEMEIJER B, et al. Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control[J]. Wind Energy Science, 2019, 4(4): 549-561. DOI:10.5194/wes-4-549-2019

KANEV S. Dynamic wake steering and its impact on wind farm power production and yaw actuator duty[J]. Renewable Energy, 2020, 146: 9-15. DOI:10.1016/j.renene.2019.06.122

KANEV S K, SAVENIJE F J, ENGELS W P. Active wake control: an approach to optimize the lifetime operation of wind farms[J]. Wind Energy, 2018, 21(7): 488-501. DOI:10.1002/we.2173

PARK J, KWON S, LAW K H. Wind farm power maximization based on a cooperative static game approach[C]//Proceedings of the SPIE, 2013, 8688. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid = 418924BF2676423839D98C004A37CDB7?doi = 10.1.1.300.3939rep = rep1type = pdf doi: 10.1117/12.2009618

PARK J, LAW K H. Cooperative wind turbine control for maximizing wind farm power using sequential convex programming[J]. Energy Conversion and Management, 2015, 101: 295-316. DOI:10.1016/j.enconman.2015.05.031

PARK J, LAW K H. Bayesian ascent: a data-driven optimization scheme for real-time control with application to wind farm power maximization[J]. IEEE Transactions on Control Systems Technology, 2016, 24(5): 1655-1668. DOI:10.1109/TCST.2015.2508007

BAY C J, KING J, FLEMING P, et al. Unlocking the full potential of wake steering: implementation and assessment of a controls-oriented model[J]. Wind Energy Science Discussions, 2019: 1-20. DOI:10.5194/wes-2019-19

WANG H, KE S, WANG T. Wind loads characteristic of large wind turbine considering typhoon transit process[J]. ACTA AERODYNAMICA SINICA, 2020, 38(5): 915-923.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

考虑轮毂运动的大型风力机叶片非线性振动分析

基于孔隙尺度多孔介质的圆柱绕流控制数值分析

四维时空不均匀尾流影响下风力机功率及气动载荷分析

阵风工况下多台风力机尾流效应的非定常特性

百米级大柔性风电叶片非线性气弹响应分析