Deep Learning Modeling of Multi-scale Characteristics of Large-scale Wind Turbine Gearbox

doi:10.16182/j.issn1004731x.joss.25-0389

Abstract

Abstract:

To address challenges in characterizing high-frequency vibrations of wind turbine gearboxes, the long computation time of rigid-flexible coupled multi-body dynamics models, and the complexity of configuring gearbox models across multiple scenarios, this study proposed a deep learning modeling method for multi-scale operation using full-condition digital testing. The study proposed a cascaded extended simulation scheme based on stream data-driven OpenFAST and Adams and utilized dynamic mode decomposition technology to construct a multi-scale dataset for the flexible multi-body dynamics characteristics of the gearbox under all operating conditions of the wind turbine. Based on this dataset, a digital surrogate model covering multiple vibration modes and time scales was constructed using the TimeMixer deep learning algorithm. The simulation experiment results show that the deep learning surrogate model for the wind turbine gearbox has the ability to accurately reflect the vibration characteristics, load characteristics, and dynamic behaviors of the gearbox under various typical working conditions. Moreover, the computational efficiency, simulation accuracy, and adaptability to variable working conditions have been improved.

Key words: wind turbine, deep learning surrogate model, gearbox multi-body dynamics, multimodal vibration, multi-time scale

CLC Number:

TP391.9

Hu yang, Li Zihao, Fu Deyi, Song Ziqiu, Fang Fang, Liu Jizhen. Deep Learning Modeling of Multi-scale Characteristics of Large-scale Wind Turbine Gearbox[J]. Journal of System Simulation, 2025, 37(10): 2454-2468.

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主要参数	量值
额定功率/MW	10
切入风速/(m/s)	4
额定风速/(m/s)	11.4
切出风速/(m/s)	25
发电机额定转速/(r/min)	493
齿轮箱总传动比	50
风轮扫掠面直径/m	178.3
塔筒高度/m	119
发电机效率/%	94.4

参数	量值
编码器层数	3
下采样层数	2
下采样窗口大小	2
初始学习率	0.001
迭代次数	100
训练轮数	15
批大小	64
训练集样本总量	140 000