ENGINEERING WAKE MODEL FOR LARGE-SCALE WIND FARMS BASED ON GRAPH TRANSFORMER

Zhang Xiaojuan; Zhang Chen; Cai Xipeng; Zhu Yihua; Luo Chao

doi:10.19912/j.0254-0096.tynxb.2024-2251

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 387-396. DOI: 10.19912/j.0254-0096.tynxb.2024-2251

ENGINEERING WAKE MODEL FOR LARGE-SCALE WIND FARMS BASED ON GRAPH TRANSFORMER

Zhang Xiaojuan¹, Zhang Chen¹, Cai Xipeng², Zhu Yihua², Luo Chao²

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Abstract

To address the challenges of achieving continuous parameterization and the limited application of graph neural networks in existing deep learning-based wind farm wake modeling, this study proposes a Graph Transformer-based wind farm wake model. Firstly, a large-scale wake dataset is constructed, encompassing diverse environmental and control parameters. A coordinate-based sampling and distance-priority strategy is employed to design the wake graph structure, generating node feature matrices that include wake field coordinates, environmental, and control parameters for neural network training. Subsequently, a Graph Transformer-based wake modeling algorithm, i.e., GT-Wake, is developed. In which the self-attention mechanism is leveraged to dynamically adjust the weight allocation of neighboring nodes in the graph structure, thereby enhancing the model’s ability to capture both local and global wake characteristics. Experimental results demonstrate that compared to MLP, CNN, and GraphSAGE, GT-Wake significantly improves modeling accuracy and achieves a favorable balance between generalization capability and computational efficiency.

Key words

wind turbine / wake / wind farm / deep learning

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Zhang Xiaojuan, Zhang Chen, Cai Xipeng, Zhu Yihua, Luo Chao. ENGINEERING WAKE MODEL FOR LARGE-SCALE WIND FARMS BASED ON GRAPH TRANSFORMER[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 387-396 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2251

References

[1] 魏赏赏, 李智寒, 陈一凯, 等. 基于状态扩张输入输出动态模态分解的风力机尾流降阶模型[J]. 太阳能学报, 2024, 45(10): 580-587.
WEI S S, LI Z H, CHEN Y K, et al.Reduced-order wake model of wind turbines based on state expansion input-output dynamic mode decomposition[J]. Acta energiae solaris sinica, 2024, 45(10): 580-587.
[2] 张虎, 许昌, 魏赏赏, 等. 基于稀疏增强动力学模态分解的风力机尾流模型研究[J]. 太阳能学报, 2024, 45(7): 681-690.
ZHANG H, XU C, WEI S S, et al.Study on wake model of wind turbine based on sparsity promoting dynamic mode decomposition[J]. Acta energiae solaris sinica, 2024, 45(7): 681-690.
[3] 张萍, 李成诚, 韩烨, 等. 漂浮式海上风力机三维尾流模型研究[J]. 太阳能学报, 2024, 45(7): 612-617.
ZHANG P, LI C C, HAN Y, et al.Research on three-dimensional wake model of floating offshore wind turbine[J]. Acta energiae solaris sinica, 2024, 45(7): 612-617.
[4] KATIC I, HOJSTRUP J, JESEN N O.A simple model for cluster efficiency[C]//European Wind Energy Association Conference and Exhibition. Roman, Itdy, 1986.
[5] BASTANKHAH M, PORTÉ-AGEL F.A new analytical model for wind-turbine wakes[J]. Renewable energy, 2014, 70: 116-123.
[6] ALFONSI G.Reynolds-averaged navier-stokes equations for turbulence modeling[J]. Applied mechanics reviews, 2009, 62(4): 040802.
[7] SAGAUT P.Large Eddy Simulation for Incompressible Flows[M]. Berlin, Heidelberg: Springer, 2002.
[8] LECUN Y, BENGIO Y, HINTON G.Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[9] LI R, ZHANG J C, ZHAO X W.Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data[J]. Energy, 2022, 258: 124845.
[10] ZHANG Z X, SANTONI C, HERGES T, et al.Time-averaged wind turbine wake flow field prediction using autoencoder convolutional neural networks[J]. Energies, 2021, 15(1): 41.
[11] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations[EB/OL].2017: arXiv: 1711.10561. https://arxiv.org/abs/1711.10561
[12] SUN S X, CUI S S, HE T, et al.An integrated deep neural network framework for predicting the wake flow in the wind field[J]. Energy, 2024, 291: 130400.
[13] WANG L Y, CHEN M, LUO Z H, et al.Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data[J]. Energy, 2024, 291: 130401.
[14] CORSO G, STARK H, JEGELKA S, et al.Graph neural networks[J]. Nature reviews methods primers, 2024, 4: 17.
[15] LI S Y, ZHANG M R, PIGGOTT M D.End-to-end wind turbine wake modelling with deep graph representation learning[J]. Applied energy, 2023, 339(C): 120928.
[16] HAMILTON W L, YING R, LESKOVEC J.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA, 2017: 1025-1035.
[17] BASTANKHAH M, PORTÉ-AGEL F.Experimental and theoretical study of wind turbine wakes in yawed conditions[J]. Journal of fluid mechanics, 2016, 806: 506-541.
[18] CRESPO A, HERNA´NDEZ J. Turbulence characteristics in wind-turbine wakes[J]. Journal of wind engineering and industrial aerodynamics, 1996, 61(1): 71-85.
[19] KING J, FLEMING P, KING R, et al.Control-oriented model for secondary effects of wake steering[J]. Wind energy science, 2021, 6(3): 701-714.
[20] SHI Y S, HUANG Z J, FENG S K, et al.Masked label prediction: unified message passing model for semi-supervised classification[C]//Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Montreal, Canada, 2021: 1548-1554.