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Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features

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Abstract

The auto-transformer rectifier unit (ATRU) is one of the most widely used avionic secondary power supplies. Timely fault identification and location of the ATRU is significant in terms of system reliability. A two-level fault diagnosis method for the ATRU using multi-source features (MSF) is proposed in this paper. Based on the topology of the ATRU, three key electrical signals are selected and analyzed to extract appropriate features for fault diagnosis. Mathematic expressions and simulation results of the feature signals under different fault modes are presented in the paper. Therefore, a unique MSF system is developed and a two-level fault diagnosis method based on radial basis function network groups is proposed. On the first level, the overall fault set is classified into three subsets and then on the second level, three radial basis function neural networks are constructed and trained to realize accurate fault localization. To verify the diagnosis performance of the proposed method, several comparative tests are implemented on a 12-pulse ATRU system, which shows that this method has a lower computational cost, better diagnostic accuracy and increased stability when compared with alternative methods.

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References

  1. Singh, B., Gairola, S.: A 28-pulse AC–DC converter for line current harmonic reduction. IET Power Electron. 1(2), 287 (2008)

    Article  Google Scholar 

  2. Chen, S.: Research on Power Supply and Emergency Switching System for Flight Test Equipment of C919 Airplane, MS. Thesis, Nanjing University of Aeronautics and Astronautics (2016)

  3. Yang, T., Bozhko, S., Asher, G.: Functional modeling of symmetrical multipulse autotransformer rectifier units for aerospace applications. IEEE Trans. Power Electron. 30(9), 4704–4713 (2014)

    Article  Google Scholar 

  4. Song, Y., Wang, B.: Survey on reliability of power electronic systems. IEEE Trans. Power Electron. 28(1), 591–604 (2012)

    Article  Google Scholar 

  5. Tsang, K.M., Chan, W.L.: Single DC source three-phase multilevel inverter using reduced number of switches. IET Power Electron. 7(4), 775–783 (2014)

    Article  Google Scholar 

  6. Zhang, D., Li, W., Xiong, X.: Overhead line preventive maintenance strategy based on condition monitoring and system reliability assessment. IEEE Trans. Power Syst. 29(4), 1839–1846 (2014)

    Article  Google Scholar 

  7. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques—part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015)

    Article  Google Scholar 

  8. Gao, Z., Cecati, C., Ding, S.: A survey of fault diagnosis and fault-tolerant techniques Part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 62(6), 3768–3774 (2015)

    Google Scholar 

  9. Edwards, C.J., Davidson, E.M., McArthur, S.D.J., Watt, I., Cumming, T.: Flexible model-based alarm processing for protection performance assessment and incident identification. IEEE Trans. Power Syst. 28(3), 2584–2591 (2013)

    Article  Google Scholar 

  10. Piris-Botalla, L.E., Falco, C.A., García, G.O., Airabella, A.M., Oggier, G.G.: Semi-conductors faults analysis in dual active bridge DC–DC converter. IET Power Electron. 9(6), 1103–1110 (2016)

    Article  Google Scholar 

  11. Cabal-Yepez, E., Garcia-Ramirez, A.G., Romero-Troncoso, R.J., Garcia-Perez, A., Osornio-Rios, R.A.: Reconfigurable monitoring system for time-frequency analysis on industrial equipment through STFT and DWT. IEEE Trans. Ind. Inf. 9(2), 760–771 (2012)

    Article  Google Scholar 

  12. Qin, X., Wang, P., Liu, Y., Guo, L., Sheng, G., Jiang, X.: Research on distribution network fault recognition method based on time-frequency characteristics of fault waveforms. IEEE Access 6, 7291–7300 (2017)

    Article  Google Scholar 

  13. Khomfoi, S., Tolbert, L.M.: Fault diagnostic system for a multilevel inverter using a neural network. IEEE Trans. Power Electron. 22(3), 1062–1069 (2007)

    Article  Google Scholar 

  14. Cai, Y., Chow, M.Y., Lu, W., Li, L.: Statistical feature selection from massive data in distribution fault diagnosis. IEEE Trans. Power Syst. 25(2), 642–648 (2010)

    Article  Google Scholar 

  15. Haroun, S., Seghir, A.N., Touati, S.: Multiple features extraction and selection for detection and classification of stator winding faults. IET Electr. Power Appl. 12(3), 339–346 (2018)

    Article  Google Scholar 

  16. Khomfoi, S., Tolbert, L.M.: Fault diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques. IEEE Trans. Ind. Electron. 54(6), 2954–2968 (2007)

    Article  Google Scholar 

  17. Toma, S., Capocchi, L., Capolino, G.A.: Wound-rotor induction generator inter-turn short-circuits diagnosis using a new digital neural network. IEEE Trans. Ind. Electron. 60(9), 4043–4052 (2013)

    Article  Google Scholar 

  18. Huang, C.M., Wang, F.L.: An RBF network with OLS and EPSO algorithms for real-time power dispatch. IEEE Trans. Power Syst. 22(1), 96–104 (2007)

    Article  Google Scholar 

  19. Li, W., Liu, W., Wu, W., Zhang, X., Gao, Z., Wu, X.: Fault diagnosis of star-connected auto-transformer based 24-pulse rectifier. Measurement 91, 360–370 (2016)

    Article  Google Scholar 

  20. Sun, Q., Wang, Y., Jiang, Y.: A novel fault diagnostic approach for DC-DC converters based on CSA-DBN. IEEE Access 6, 6273–6285 (2017)

    Article  Google Scholar 

  21. Di, W.: The Power Electronic Circuit Fault Diagnosis Based on the Neural Network, MS. Thesis, Harbin University of Science and Technology (2017)

  22. Chen, Y.Q., Fink, O., Sansavini, G.: Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Trans. Ind. Electron. 65(1), 561–569 (2017)

    Article  Google Scholar 

  23. Wang, Z., Huang, Z., Song, C., Zhang, H.: Multiscale adaptive fault diagnosis based on signal symmetry reconstitution preprocessing for microgrid inverter under changing load condition. IEEE Trans. Smart Grid 9(2), 797–806 (2016)

    Article  Google Scholar 

  24. Meng, K., Dong, Z.Y., Wang, D.H., Wong, K.P.: A self-adaptive RBF neural network classifier for transformer fault analysis. IEEE Trans. Power Syst. 25(3), 1350–1360 (2010)

    Article  Google Scholar 

  25. Yang, S., Xiang, D., Bryant, A., Mawby, P., Ran, L., Tavner, P.: Condition monitoring for device reliability in power electronic converters: a review. IEEE Trans. Power Electron. 25(11), 2734–2752 (2010)

    Article  Google Scholar 

  26. Poggio, T., Girosi, F.: Networks for approximation and learning. Proc. IEEE 78(9), 1481–1497 (1999)

    Article  Google Scholar 

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Acknowledgements

We would like to acknowledge the support from the National Science Foundation of China (NSFC) under the Project No. U1933115.

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Yi Lin, Hongjuan Ge & Shuwen Chen

  2. Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, USA

    Michael Pecht

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  1. Yi Lin
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  2. Hongjuan Ge
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  3. Shuwen Chen
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  4. Michael Pecht
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Correspondence to Shuwen Chen.

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Lin, Y., Ge, H., Chen, S. et al. Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features. J. Power Electron. 20, 754–763 (2020). https://doi.org/10.1007/s43236-020-00057-z

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  • DOI: https://doi.org/10.1007/s43236-020-00057-z

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