Optimized-XGBoost Early Warning of Wind Turbine Generator Front Bearing Fault

Authors

Le Wei, 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; ;2. Department of Automation, North China Electric Power University, Baoding 071003, China;
Yin Shi, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; ;

Abstract

Abstract: In order to identify the abnormal running state of the generator in time, a wind turbine generator front bearing fault early warning method based on Bayesian optimized extreme gradient boosting algorithm is proposed. The historical data collected by SCADA (Supervisory Control And Data Acquisition) are preprocessed by effective data preprocessing methods. The temperature prediction model of the front bearing of wind turbine generator is constructed based on the Bayesian-optimized XGBoost (eXtreme Gradient Boosting) algorithm and the fault early warning threshold of the front bearing of the wind turbine generator is determined based on the 3σ criterion. The experimental results show that the proposed method can detect the abnormal signals of the front bearing of the wind turbine generator in advance. Compared with the models established by random search and grid search, the advantages of Bayesian optimization model in generalization performance and prediction accuracy are verified.

Recommended Citation

Wei, Le; Hu, Xiaodong; and Shi, Yin (2021) "Optimized-XGBoost Early Warning of Wind Turbine Generator Front Bearing Fault," Journal of System Simulation: Vol. 33: Iss. 10, Article 6.
DOI: 10.16182/j.issn1004731x.joss.20-0538
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss10/6

First Page

2335

Revised Date

2020-09-12

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0538

Last Page

2343

CLC

TH133.3;TP391

Recommended Citation

Wei Le, Hu Xiaodong, Yin Shi. Optimized-XGBoost Early Warning of Wind Turbine Generator Front Bearing Fault[J]. Journal of System Simulation, 2021, 33(10): 2335-2343.

Corresponding Author

Xiaodong Hu,

DOI

10.16182/j.issn1004731x.joss.20-0538