Online Fault Diagnosis of the Hybrid Electrical Multiple Unit Traction Converter

  • Lei Wang
  • Mengzhu Wang
  • Yujia Guo
  • Ruichang Qiu
  • Lijun Diao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 482)

Abstract

In this paper, the signatures and the diagnosis approach of the failure of switching devices in Hybrid Electrical Multiple Unit (HEMU) traction converter (TC) are developed. In this paper, the distorted voltage and current with such failures are treated as disturbance exerted over normal voltage and current without failures, and a special analytical failure model is built for the expression of voltage and current disturbance. Combined with the failure model, this paper proposes a novel reasoning process to locate malfunctioning switching device. The reasoning process is based on object-oriented colored Petri Net (OOCPN). Digitalized failure signatures are taken as inputs into the OOCPN reasoning machine, what stimulates the brain activities during fault diagnosis of an expert.

Keywords

Analytical failure model Switching device failure Cascading and coupling interaction Online fault reasoning Object-oriented Petri Net 

Notes

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities of China (No.E16JB00160/2016JBM062/2016JBM058) and The National Key Research and Development Program of China (2016YFB1200504-C-02).

References

  1. 1.
    Lei R, Zheng W, Gong C, Shen Q (2015) Fault feature extraction techniques for power devices in power electronic converters: a review. Proc CSEE 35(12):3089–3101Google Scholar
  2. 2.
    Celaya JR, Saxena A, Vashchenko V et al (2011) Prognostics of power MOSFET. In: The 23th international symposium on power semiconductor devices and ICs. IEEE, San Diego, CA, pp 160–163Google Scholar
  3. 3.
    Xiong Y, Cheng X, Shen ZJ et al (2008) Prognostics and warning system for power-electronics modules in electronic, hybrid electric, and fuel-cell vehicles. IEEE Trans Ind Electr 55(6):2268–2276Google Scholar
  4. 4.
    Zhou S, Zhou L, Sun P (2013) Monitoring potential defects in an IGBT module based on dynamic changes of the gate current. IEEE Trans Power Electr 28(3):1479–1487CrossRefGoogle Scholar
  5. 5.
    Rodriguez-Blanco MA, Claudio-sanchez A, Theilliol D et al (2011) A failure-detection strategy for IGBT based on gate-voltage behavior applied to a motor drive system. IEEE Tran Ind Electr 58(5):1625–1633CrossRefGoogle Scholar
  6. 6.
    Han X, Wang Y, Cui J (2008) Fault diagnosis of power electronic circuits based on wavelet radical basis function network. Microelectronics 38(3):309–311Google Scholar
  7. 7.
    Hu Q, Wang R, Zhan Y (2008) Fault diagnosis technology based on SVM in power electronics circuit. Proc CSEE 28(12):107–111Google Scholar
  8. 8.
    Zhang X (1996) Time series analysis. Tsinghua university press, Beijing, pp 11–13Google Scholar
  9. 9.
    Ma H, Mao X, Xu D (2005) Parameter identification of DC/DC power electronic circuits based on hybrid system model. Proc CSEE 25(10):50–54Google Scholar
  10. 10.
    Bimal KB (2011) Modern power electronics and AC drives. Prentice Hall PTRGoogle Scholar
  11. 11.
    Jiang J, Holtz J (2001) An efficient braking method for controlled AC drives with a diode rectifier front end. IEEE Trans Ind Appl 37(5):1299–1307CrossRefGoogle Scholar
  12. 12.
    Oikonomou N, Holtz J (2008) Closed-loop control of medium-voltage drives operated with synchronous optimal pulsewidth modulation. IEEE Trans Ind Appl 44(1):115–123CrossRefGoogle Scholar
  13. 13.
    Wang L (2011) Study on the fault diagnosis and protection of energy-fed supply system in urban mass transit. Doctoral Dissertation, Beijing Jiaotong UniversityGoogle Scholar
  14. 14.
    Wang L, Li Y, Liu Z (2012) The fault diagnosis method of urban rail transit traction power supply system based on topology analysis and backward reasoning of OOCPN. China Railway Sci 33(4):52–59Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Lei Wang
    • 1
  • Mengzhu Wang
    • 1
  • Yujia Guo
    • 1
  • Ruichang Qiu
    • 1
  • Lijun Diao
    • 1
  1. 1.School of Electrical EngineeringBeijing Jiaotong UniversityHaidian District, BeijingChina

Personalised recommendations