Prediction of Inflow Wind Field for Large-scale Wind Turbines Based on Multimodal Hybrid Deep Learning

doi:10.16182/j.issn1004731x.joss.25-0707

Abstract

Abstract:

To address the demand for high-precision inflow wind field prediction in large-scale wind turbines, traditional CFD methods suffer from high computational costs and poor real-time applicability. This paper proposed a multimodal hybrid deep learning-based wind field prediction method. The proposed method took turbine operating parameters and far-range wind field images as inputs and generated short-range wind field images as outputs. By employing a U-Net-Transformer-GAN hybrid architecture, the model achieved multi-scale feature extraction, temporal dependency modeling, and high-resolution wind field image generation. The vorticity transport equation and Kármán-Howarth turbulence statistics were incorporated as weak constraints to enhance physical consistency, while a temporal delay-difference mechanism was introduced to mitigate input and output asynchrony. Experimental results demonstrate that the proposed method outperforms comparative models, with PSNR of 32.51 dB, SSIM of 0.894, and LPIPS of 0.025. It accurately reproduces the Kolmogorov-5/3 power law in the turbulent energy spectrum, achieving a roughly 20-fold increase in predictive efficiency over traditional large eddy simulation.

Key words: wind power generation, prediction of inflow wind field, multimodal hybrid neural network, spatiotemporal attention, physical embedding constraint

CLC Number:

TP391.9

Wang Jiheng, Hu Yang, Song Ziqiu, Fang Fang, Liu Jizhen. Prediction of Inflow Wind Field for Large-scale Wind Turbines Based on Multimodal Hybrid Deep Learning[J]. Journal of System Simulation, 2026, 38(2): 501-517.

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参数	值
叶片数量	3
额定功率/MW	10
风轮扫掠面直径/m	178.3
轮毂直径/m	5.6
轮毂高度/m	119
切入、额定、切出风速/(m/s)	4、11.4、25
发电机额定转速/(r/min)	493

工况/(m/s)	PSNR/dB	SSIM	LPIPS
10	30.15	0.86	0.040
14	29.90	0.86	0.042

模型	PSNR/dB	SSIM	LPIPS	Vort	KH
U-Net	28.12	0.81	0.105	1.74×10^-4	2.31×10^-3
U-Net+LSTM	29.34	0.87	0.095	1.49×10^-4	2.04×10^-3
U-Net-Transformer-GAN	33.75	0.90	0.025	9.86×10^-5	1.47×10^-3

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