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

Authors

Yang Hu, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Zihao Li, School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Deyi Fu, China Electric Power Research Institute, Beijing 100192, China
Ziqiu Song, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Fang Fang, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Jizhen Liu, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

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.

Recommended Citation

Hu, Yang; Li, Zihao; Fu, Deyi; Song, Ziqiu; Fang, Fang; and Liu, Jizhen (2025) "Deep Learning Modeling of Multi-scale Characteristics of Large-scale Wind Turbine Gearbox," Journal of System Simulation: Vol. 37: Iss. 10, Article 2.
DOI: 10.16182/j.issn1004731x.joss.25-0389
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss10/2

First Page

2454

Last Page

2468

CLC

TP391.9

Recommended Citation

Hu Yang, Li Zihao, Fu Deyi, et al. Deep Learning Modeling of Multi-scale Characteristics of Largescale Wind Turbine Gearbox[J]. Journal of System Simulation, 2025, 37(10): 2454-2468.

Corresponding Author

Song Ziqiu

DOI

10.16182/j.issn1004731x.joss.25-0389