Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features

  • Lin, Yi (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics) ;
  • Ge, Hongjuan (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics) ;
  • Chen, Shuwen (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics) ;
  • Pecht, Michael (Center for Advanced Life Cycle Engineering, University of Maryland)
  • Received : 2019.07.11
  • Accepted : 2019.12.23
  • Published : 2020.05.20
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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.

Keywords

Acknowledgement

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

References

  1. Singh, B., Gairola, S.: A 28-pulse AC-DC converter for line current harmonic reduction. IET Power Electron. 1(2), 287 (2008) https://doi.org/10.1049/iet-pel:20070239
  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) https://doi.org/10.1109/TPEL.2014.2364682
  4. Song, Y., Wang, B.: Survey on reliability of power electronic systems. IEEE Trans. Power Electron. 28(1), 591-604 (2012) https://doi.org/10.1109/TPEL.2012.2192503
  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) https://doi.org/10.1049/iet-pel.2013.0319
  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) https://doi.org/10.1109/TPWRS.2013.2295379
  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) https://doi.org/10.1109/TIE.2015.2417501
  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) https://doi.org/10.1109/TIE.2015.2419013
  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) https://doi.org/10.1109/TPWRS.2013.2243763
  10. Piris-Botalla, L.E., Falco, C.A., Garcia, 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) https://doi.org/10.1049/iet-pel.2015.0299
  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) https://doi.org/10.1109/TII.2012.2221131
  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) https://doi.org/10.1109/access.2017.2728015
  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) https://doi.org/10.1109/TPEL.2007.897128
  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) https://doi.org/10.1109/TPWRS.2009.2036924
  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) https://doi.org/10.1049/iet-epa.2017.0457
  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) https://doi.org/10.1109/TIE.2007.906994
  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) https://doi.org/10.1109/TIE.2012.2229675
  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) https://doi.org/10.1109/TPWRS.2006.889133
  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) https://doi.org/10.1016/j.measurement.2016.05.069
  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) https://doi.org/10.1109/access.2017.2786458
  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) https://doi.org/10.1109/TIE.2017.2721922
  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) https://doi.org/10.1109/tsg.2016.2565667
  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) https://doi.org/10.1109/TPWRS.2010.2040491
  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) https://doi.org/10.1109/TPEL.2010.2049377
  26. Poggio, T., Girosi, F.: Networks for approximation and learning. Proc. IEEE 78(9), 1481-1497 (1999) https://doi.org/10.1109/5.58326

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