RESEARCH ON WIND FARM LAYOUT OPTIMIZATION CONSIDERING FATIGUE LOADS CONSTRAINT OF WIND TURBINES

Liu Weijie; Huang Guoqing; Peng Liuliu; Yang Qingshan; Jiang Yan

doi:10.19912/j.0254-0096.tynxb.2024-0279

PDF(1950 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 548-555. DOI: 10.19912/j.0254-0096.tynxb.2024-0279

RESEARCH ON WIND FARM LAYOUT OPTIMIZATION CONSIDERING FATIGUE LOADS CONSTRAINT OF WIND TURBINES

Liu Weijie¹, Huang Guoqing¹, Peng Liuliu¹, Yang Qingshan¹, Jiang Yan²

Author information +

History +

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.

Key words

wind farm / layout optimization / fatigue load / surrogate model / TOPFARM / Horns Rev 1 wind farm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Liu Weijie, Huang Guoqing, Peng Liuliu, Yang Qingshan, Jiang Yan. RESEARCH ON WIND FARM LAYOUT OPTIMIZATION CONSIDERING FATIGUE LOADS CONSTRAINT OF WIND TURBINES[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 548-555 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0279

References

[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.