Fault Diagnosis Method in Controlled Rectifier Based on Support Vector Machines

Hong Da Liu; Wen Jie Yue; Hai Lan; Dian Hua Zhang

doi:10.4028/www.scientific.net/AMR.383-390.5006

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 383-390Fault Diagnosis Method in Controlled Rectifier...

Fault Diagnosis Method in Controlled Rectifier Based on Support Vector Machines

Abstract:

Based on support vector machines (SVM) theory, a new method of fault diagnosis for controlled rectifier circuits is proposed in this paper. Fault diagnose method of power electronic converter has been studied on the basis of direct waveform analysis and SVM. Three-phase bridge rectifier circuit fault is presented as an example, coupled with the results of a certain power electronic circuit experiment, indicates that the method can accurately diagnose and locate fault for controlled rectifier circuits, which is justified practical and applicable for controlled rectifier circuit fault diagnosis.

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Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

5006-5011

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.383-390.5006

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Online since:

November 2011

Authors:

Hong Da Liu, Wen Jie Yue, Hai Lan, Dian Hua Zhang

Keywords:

Controlled Rectifier Circuit, Fault Diagnosis, Support Vector Machine (SVM)

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References

[1] Xu De-hong and Cheng Zhao-ji, Cui Xue-jun (1994). Fault diagnosis of power electronic circuits with discrete fourier transform. Journal of Zhejiang University (Nature Science), 28(6), 666-674.

Google Scholar

[2] Li Min-yuan and Chen Ru-qing (2004). ON-LINE FAULT DIAGNOSIS SYSTEM FOR HIGH-POWER CONTROLLED RECTIFIER BASED ON IMPROVED FREQUENCY ANALYSIS METHOD. PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING, 24(10), 163-167.

Google Scholar

[3] Ma Hao and Lee Y S (1999). Fault diagnosis of power electronic circuits based on neural network and waveform analysis. Proceedings of IEEE PEDS' 99, Hong Kong, 234-237.

DOI: 10.1109/peds.1999.794566

Google Scholar

[4] Long Bohua et al (2009). Fault Diagnosis of Power Electronic Circuits Based on Quantum Neural Network. Transactions of China Electrotechnical Society, 24(10), 170-174.

Google Scholar

[5] Chih-Wei Hsu and Chih-Jen Lin (2002). A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks, 13(2), 415-425.

DOI: 10.1109/72.991427

Google Scholar