Research on Intelligent Fault Identification Technology of Wind Turbine Supported by Fault Knowledge Base

Research on Intelligent Fault Identification Technology of Wind Turbine Supported by Fault Knowledge Base

Fei Chen Zhongguang Fu Zhiling Yang 

School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing, China, 2 Beinong Rd., Changping District, Beijing 102206,

Corresponding Author Email: 
chenfei@ncepu.edu.cn; fzg@ncepu.edu.cn; yzhil@ncepu.edu.cn
Page: 
1-15
|
DOI: 
https://doi.org/10.18280/mmc_a.900101
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

Wind power companies are increasingly introducing fault diagnosis technologies, however, the full use of these technologies mainly depends on expert experience. This paper puts forward a fault identification method for the intelligent fault identification of wind turbine based on fault knowledge base. The model uses principal component analysis to integrate the eigenvalues ​​of vibration and SCADA signals, and then takes the existing fault sample with the highest matching rate in the wind farm fault knowledge base as the input to train the least squares support vector regression algorithm model optimized by particle swarm optimization. The matching rates of fault samples in the fault knowledge base are updated after each diagnosis. Finally, the measured data of the wind farm are used to verify the effect of the model. It is proved that, if the fault samples are sufficiently trained, this method can accurately diagnose the existing faults from the fault base.

Keywords: 

intelligent fault identification; knowledge base; data fusion; wind turbine

1. Introduction
2. Data Collection and Fusion
3. Fault Knowledge Base
4. Fault Knowledge Base
5. Application and Analysis
6. Conclusion
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