COOPERATIVE CONTROL OF WIND FARM BASED ON ACTIVE PITCH

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

doi:10.19912/j.0254-0096.tynxb.2023-0753

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (9) : 511-516. DOI: 10.19912/j.0254-0096.tynxb.2023-0753

COOPERATIVE CONTROL OF WIND FARM BASED ON ACTIVE PITCH

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

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Abstract

In order to increase the overall power of wind farm without increasing the unit load, a cooperative control method based on active variable pitch is proposed in this paper. In this method, pitch angle is used as the control variable of cooperative control. The analytical wake model is used to simulate the flow field in a wind farm in real time. Differential evolution algorithm is used to optimize the optimum pitch angle of plant units. In a dynamic wind environment with an average wind speed of 8 m/s and turbulence intensity of 8%, the simulation shows that compared with the traditional single-unit optimal control strategy, the cooperative control method based on active pitch variation can increase the overall power generation of a wind farm with three units in tandem by more than 10%, while significantly reducing blade load. The research results fully show that the wind farm cooperative control method based on active variable pitch has certain practical application value.

Key words

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

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Zhang Ziliang, Guo Naizhi, Yi Kan, Wen Renqiang, Shi Kezhong. COOPERATIVE CONTROL OF WIND FARM BASED ON ACTIVE PITCH[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 511-516 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0753

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 A M V, 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(1/2/3): 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] JONKMAN J, BUTTERFIELD S, MUSIAL W, et al.Definition of a 5-MW reference wind turbine for offshore system development[R]. Office of Scientific & Technical Information Technical Reports, 2009.
[12] JENSEN N O.A note on wind generator interaction[M]. Risø National Laboratory, 1983.
[13] JONKMAN J M, SHALER K, Fast. farm user's guide and theory manual[R]. 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.