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

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

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.

Key words: wind turbine, front bearing of generator, attention mechanism, Bi-LSTM, health degree

CLC Number:

TK83
TP391.9

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.

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