为了令协同控制下的风力机能在实时变化的来流中安全可靠的达到提升发电量的目的,提出一种基于稳定偏航的协同控制方法。在该方法中,解析尾流模型被用来实时模拟风电场中的流场状态;差分进化算法用以优化场内机组的最佳偏航角度;将上述优化结果与机组自身的偏航控制逻辑相结合,能令机组稳定地执行最优协同控制指令。在动态风环境下的模拟表明,相较于传统的单机最优控制策略,该文提出的协同控制方法令风电场整体发电量提升4.76%。且该方法能令各机组仅需少量的主动偏航动作在实时变化的湍流风况中达成减少尾流影响的目的。研究结果说明该文提出的基于稳定偏航的风电场协同控制方法具有一定的实际应用价值。
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.
关键词
风电场 /
协同控制 /
偏航 /
尾流模型 /
优化算法
Key words
wind farm /
cooperative control /
yaw /
wake model /
optimization algorithm
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参考文献
[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.
基金
中国长江三峡集团有限公司科研项目资助（合同编号：202103506）
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