Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation

Authors

Yin Shi, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; ;2. Zhong Neng Power-Tech Development Co., LTD, Beijing 100034, China;
Guolian Hou, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; ;
Chi Yan, 2. Zhong Neng Power-Tech Development Co., LTD, Beijing 100034, China;
Linjuan Gong, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; ;
Xiaodong Hu, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; ;

Abstract

Abstract: Aiming at the deterioration trend of front bearing of doubly-fed wind turbine generator, a new combined modeling method is proposed to predict health degree of front bearing of generator. The GMM is used to identify operating conditions of wind turbines. The temperature model of front bearing based on ELM is established respectively in each sub-condition. Combining with temperature residual characteristics and time-frequency characteristics of vibration signal, the health degree of front bearing is calculated. Based on attention mechanism, the Bi-LSTM neural network is proposed to model and predict health degree of front bearing. The result shows that the combined modeling method has high accuracy and generalization ability.

Recommended Citation

Shi, Yin; Hou, Guolian; Yan, Chi; Gong, Linjuan; and Hu, Xiaodong (2021) "Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation," Journal of System Simulation: Vol. 33: Iss. 6, Article 11.
DOI: 10.16182/j.issn1004731x.joss.20-0148
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss6/11

First Page

1323

Revised Date

2020-06-05

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0148

Last Page

1333

CLC

TK83;TP391.9

Recommended Citation

Yin Shi, Hou Guolian, Chi Yan, Gong Linjuan, Hu Xiaodong. Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation[J]. Journal of System Simulation, 2021, 33(6): 1323-1333.

DOI

10.16182/j.issn1004731x.joss.20-0148