在进行风电场布局优化时,如果目标函数仅考虑年发电量AEP,可能导致优化后风电场的部分风力机疲劳荷载增大,进而增加运维成本,因此优化得到的风电场布局可能不是最优解。针对上述问题,开展考虑风力机疲劳载荷限制的风电场布局优化研究。首先,通过输入每台风力机的有效风速和有效湍流强度至代理模型来预测等效疲劳载荷。然后,使用开源风电场优化平台TOPFARM对风电场进行布局优化,并在优化过程中增加对寿命周期等效疲劳载荷LDEL限制来降低风力机的疲劳载荷。最后,对Horns Rev 1典型风电场进行考虑疲劳限制的布局优化研究。结果表明：与不考虑疲劳限制相比,优化后的风电场会牺牲少量的AEP,但可换来LDEL较大程度的降低。
Abstract
When optimizing the layout of a wind farm, typically only objective functions such as Annual Energy Production （AEP） are considered, easily causing increased fatigue damages for wind turbines and higher operation and maintenance costs for the wind farm, thus the final layout of wind farm might not be the optimal if the optimization codes don't consider the wind turbines fatigue loads constraint. On account of that, this study proposes practical measures to address this issue. Firstly, the damage equivalent loads are predicted by inputting load surrogates for the effective wind speed and effective turbulence intensity of each wind turbine. After that, the open-source wind farm optimization platform TOPFARM is utilized in this study to optimize the wind farm layout. Furthermore, the constraint is added to limit the Lifetime Damage-equivalent Fatigue Loads （LDEL） of the wind turbines during the optimization process. Finally, this study optimized the layout of Horns Rev 1 wind farm considering constraints on wind turbine fatigue loads, concluding that the optimized wind farm only sacrificed a small amount of AEP in exchange for a significant reduction in LDEL.
关键词
风电场 /
布局优化 /
疲劳载荷 /
代理模型 /
TOPFARM /
Horns Rev 1风电场
Key words
wind farm /
layout optimization /
fatigue load /
surrogate model /
TOPFARM /
Horns Rev 1 wind farm
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参考文献
[1] 童宁. 大规模远距离外送风电场继电保护及反脱网关键技术研究[D]. 武汉: 华中科技大学, 2016.
TONG N.Research on key technologies of relay protection and anti-off-grid for large-scale long-distance wind farm[D]. Wuhan: Huazhong University of Science and Technology, 2016.
[2] 秦海岩. 2023,我们御风前行谱新篇[J]. 风能, 2023 (12): 1.
QIN H Y.We move forward though using wind and compose a new chapter in2023[J]. Wind energy, 2023(12): 1.
[3] KAMAL M, RAHMAN M M.Advances in fatigue life modeling: a review[J]. Renewable and sustainable energy reviews, 2018, 82: 940-949.
[4] ZHAO R Y, SHEN W Z, KNUDSEN T, et al.Fatigue distribution optimization for offshore wind farms using intelligent agent control[J]. Wind energy, 2012, 15(7): 927-944.
[5] COLLET D, ALAMIR M, DI DOMENICO D, et al.A fatigue-oriented cost function for optimal individual pitch control of wind turbines[J]. IFAC-PapersOnLine, 2020, 53(2): 12632-12637.
[6] HUANG L L, TANG H, ZHANG K H, et al.3-D layout optimization of wind turbines considering fatigue distribution[J]. IEEE transactions on sustainable energy, 2020, 11(1): 126-135.
[7] DIMITROV N, KELLY M C, VIGNAROLI A, et al.From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases[J]. Wind energy science, 2018, 3(2): 767-790.
[8] RIVA R, LIEW J, FRIIS-MØLLER M, et al. Wind farm layout optimization with load constraints using surrogate modelling[J]. Journal of physics: conference series, 2020, 1618(4): 042035.
[9] STANLEY A P J, KING J, BAY C, et al. A model to calculate fatigue damage caused by partial waking during wind farm optimization[J]. Wind energy science, 2022, 7(1): 433-454.
[10] STANLEY A P J, KING J, NING A. Wind farm layout optimization with loads considerations[J]. Journal of physics: conference series, 2020, 1452(1): 012072.
[11] 林剑威, 沈文忠. 考虑疲劳载荷的风电场布局优化[C]//中国可再生能源学术大会-风能论文集. 南京, 中国, 2023.
LIN J W, SHEN W Z.Optimization of wind farm layout considering fatigue load[C]//Proceedings of China Renewable Energy Society -Wind Energy. Nanjing, China, 2023.
[12] PEDERSEN M M, FORSTING A M, VAN D L P, et al. PyWake 2.5.0: an open-source wind farm simulation tool[EB/OL]. https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.
[13] RICCARDO R, JAIME Y L, MIKKEL F, et al.Welcome to TOPFARM[EB/OL]. https://topfarm.pages.windenergy.dtu.dk/TopFarm2/index.html
[14] JONKMAN J, BUTTERFIELD S, MUSIAL W, et al.Definition of a 5-MW reference wind turbine for offshore system development[J]. Contract, 2009(February): 1-75.
[15] GORISSEN D, COUCKUYT I, DEMEESTER P, et al.A surrogate modeling and adaptive sampling toolbox for computer based design[J]. Journal of machine learning research, 2010, 11: 2051-2055.
[16] 黄国庆, 刘伟杰, 王彬滨, 等.基于深度神经网络的风力机疲劳载荷代理模型研究[J]. 太阳能学报, 2025, 46(4): 398-405.
HUANG G Q, LIU W J, WANG B B,et al.Research on surrogate models for fatigue loads prediction of wind turbines based on deep neural network[J]. Acta energiae solaris sinica, 2025, 46(4): 398-405.
[17] IEC 61400-1-2019, Wind turbine generator system, edition 4-part1: design requirements[S].
[18] FRANDSEN S T.Turbulence and turbulence- generated structural loading in wind turbine clusters[D]. Roskilde: Risø National Laboratory, 2007.
[19] BASTANKHAH M, PORTÉ-AGEL F.A new analytical model for wind-turbine wakes[J]. Renewable energy, 2014, 70: 116-123.
[20] KATIC I, HØJSTRUP J, JENSEN N O. A simple model for cluster efficiency[C]//EWEC'86: European Wind Energy Association Conference and Exhibition. Rome, Italy, 1986, 1: 407-410.
基金
国家自然科学基金面上项目（52378480）; 重庆市自然科学基金创新发展联合基金（CSTB2024NSCQ-LZX0010）; 学科创新引智基地项目（B18062）; 重庆市教委科学技术研究重大项目（KJZD-M202300201）
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