COORDINATED CONTROL OF WIND FARM BASED ON STEADY YAW

Zhang Ziliang; Guo Naizhi; Yi Kan; Wen Renqiang; Shi Kezhong

doi:10.19912/j.0254-0096.tynxb.2023-0207

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 530-535. DOI: 10.19912/j.0254-0096.tynxb.2023-0207

COORDINATED CONTROL OF WIND FARM BASED ON STEADY YAW

Zhang Ziliang¹, Guo Naizhi², Yi Kan¹, Wen Renqiang¹, Shi Kezhong²

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Abstract

Wind farm coordinated control is one of the important technologies to reduce the impact of wakes in wind farms. In order to make wind turbines under cooperative control achieve the purpose of increasing power generation safely and reliably in the real-time changing incoming flow, a cooperative control method based on stable yaw is proposed. In this method, the analytical wake model is used to simulate the flow field in real time; the differential evolution algorithm is used to optimize the yaw angle of wind turbines; the key point is to consider the stable yaw control logic of the wind turbine so that it can stably execute the optimal cooperative control command. The simulation results in the dynamic wind environment show that, compared with the traditional control strategy, the cooperative control method can increase the overall power generation of the wind farm by 4.76%. And this method can make each unit only need a small amount of active yaw action to achieve the purpose of reducing the influence of wake in the real-time changing turbulent wind conditions. The research results show that the wind farm cooperative control method based on active yaw proposed in this paper has certain practical application value.

Key words

wind farm / cooperative control / yaw / wake model / optimization algorithm

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Zhang Ziliang, Guo Naizhi, Yi Kan, Wen Renqiang, Shi Kezhong. COORDINATED CONTROL OF WIND FARM BASED ON STEADY YAW[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 530-535 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0207

References

[1] GUO N Z, SHI K Z, LI B, et al.A physics-inspired neural network model for short-term wind power prediction considering wake effects[J]. Energy, 2022, 261: 125208.
[2] KUIK G, PEINKE J, NIJSSEN R, et al.Long-term research challenges in wind energy-a research agenda by the European Academy of Wind Energy[J]. Wind energy science, 2016, 1(1): 1-39 .
[3] 凌子焱, 赵振宙, 刘惠文, 等. 风力机三维尾流模型研究与验证[J]. 太阳能学报, 2023, 44(4): 99-105.
LING Z Y, ZHAO Z Z, LIU H W, et al.Research and validation of 3d wake model for wind turbine[J]. Acta energiae solaris sinica, 2023, 44(4): 99-105.
[4] GUO N Z, ZHANG M M, LI B, et al.Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model[J]. Journal of wind engineering and industrial aerodynamics, 2021, 211: 104548.
[5] KHEIRABADI A C, NAGAMUNE R.A quantitative review of wind farm control with the objective of wind farm power maximization[J]. Journal of wind engineering and industrial aerodynamics, 2019, 192: 45-73.
[6] STEINBUCH M, DE BOER W W, BOSGRA O H, et al. Optimal control of wind power plants[J]. Journal of wind engineering and industrial aerodynamics, 1988, 27(1): 237-246.
[7] 刘颖明, 王树旗, 王晓东. 基于广义预测控制的风电场调频控制策略研究[J]. 太阳能学报, 2022, 43(3): 405-410.
LIU Y M, WANG S Q, WANG X D.Secondary frequency regulation control of wind farm based on genrealized predictive control[J]. Acta energiae solaris sinica, 2022, 43(3): 405-410.
[8] ADARAMOLA M S, KROGSTAD P Å.Experimental investigation of wake effects on wind turbine performance[J]. Renewable energy, 2011, 36(8): 2078-2086.
[9] 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.
[10] 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.
[11] BASTANKHAH M, PORTÉ-AGEL F.A new analytical model for wind-turbine wakes[J]. Renewable energy, 2014, 70: 116-123.
[12] FUERTES F C, MARKFORT C D, PORTÉ-AGEL F.Wind turbine wake characterization with nacelle-mounted wind lidars for analytical wake model validation[J]. Remote sensing, 2018, 10(5): 668.
[13] JONKMAN J M, SHALER K, Fast. farm user's guide and theory manual[M]. Golden, CO, USA: National Renewable Energy Laboratory, 2021.
[14] JONKMAN J, DOUBRAWA P, HAMILTON N, et al.Validation of FAST. Farm against large-eddy simulations[J]. Journal of physics: conference series, 2018, 1037: 062005.