Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Inception module and deep residual shrinkage network-based arc fault detection method for pantograph-catenary systems

  • Li, Bin (School of Electrical and Control Engineering, Liaoning Technical University) ;
  • Cui, Feifan (School of Electrical and Control Engineering, Liaoning Technical University)
  • Received : 2021.11.25
  • Accepted : 2022.03.02
  • Published : 2022.06.20
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Abstract

When a train is running at a high speed, due to the of-line condition between the pantograph and the catenary, a pantograph catenary arc (PACA) can seriously endanger the normal operation of the train, and the current collection quality of pantograph catenary system. To achieve stable current collection, PACAs are studied in this paper. Under the condition of the same contact current, five groups of experiments with different contact pressures and sliding speeds were carried out, and current signals under the normal state and the fault state were obtained. The collected current signal is converted into a picture through the Markov transition field (MTF) to construct a picture data set. To fully mine the feature information in image samples, a PACA recognition algorithm based on Inception_DRSN is proposed. First, the features of the input image sample are extracted through an inception module with multi-scale parallel convolution operation. Then convolution results of different sizes are concatenated and imported into the deep residual shrinkage network (DRSN) with an attention mechanism (AM) and a soft thresholding (ST) to perform deeper extraction and to highlight the current features under the PACA. Finally, the recognition results are classified by the global pooling (GAP) and full connection (FC) layer. Experimental results show that the average accuracy of this method is 98.50%, which is better than that of the other comparison methods. In addition, this method has robustness and superiority in the field of PACA recognition.

Keywords

Acknowledgement

The study was funded by the National Natural Science Foundation of China under Grant 51674136, 52077158, in part by the Liaoning Revitalization Talents Program under Grant XLYC1802110 (Grant nos. 51674136, 52077158, XLYC1802110).

References

  1. Gao, G., Xu, P., Wei, W., Yang, Z., Wu, G.: The effect of running speed of high speed trains on the surface erosion of pantograph strip under pantograph arc. In: 2018 IEEE Holm Conference on Electrical Contacts. Albuquerque. NM. USA, IEEE, pp 1-5. (2018)
  2. Mariscotti, A., Marrese, A., Pasquino, N.: Time and frequency characterization of radiated disturbance in telecommunication bands due to pantograph arcing. Measurement 46(10), 4342-4352 (2013) https://doi.org/10.1016/j.measurement.2013.04.054
  3. Wei, W., Wu, J., Gao, G., Gu, Z., Liu, X., Zhu, G., Wu, G.: Study on Pantograph arcing in a laboratory simulation system by high-speed photography. IEEE Trans. Plasma Sci. 44(10), 2438-2445 (2016) https://doi.org/10.1109/TPS.2016.2601314
  4. Chen, Z.H., Wu, D., Hui, L.H.: Study on sliding electrical contact failure under fluctuating loads. J. Electric. Technol. 34(21), 4492-4500 (2019)
  5. Barmada, S., Landi, A., Papi, M.: Wavelet multiresolution analysis for monitoring the occurrence of arcing on overhead electrified railways. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit. 217(3), 177-187 (2003) https://doi.org/10.1243/095440903769012885
  6. Ilhan, A., Yaman, O., Mehmet, K.: Particle swarm based arc detection on time series in pantograph-catenary system. In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE (2014)
  7. Barmada, S., Raugi, M., Tucci, M., Romano, F.: Arc detection in pantograph-catenary systems by the use of support vector machines-based classification. Electric. Syst. Transport. Iet. 4, 45-52 (2014) https://doi.org/10.1049/iet-est.2013.0003
  8. Karaduman, G., Karakose, M., Akin, E.: Deep learning based Arc detection in pantograph-catenary systems. IEEE, 2018 based series AC arc detection algorithms. J. Power Electron. 21, 1621-1631 (2021). https://doi.org/10.1007/s43236-021-00299-5
  9. Park, C.J., Dang, H.L., Kwak, S.: Deep learning-based series AC arc detection algorithms. J. Power Electron. 21, 1621-1631 (2021). https://doi.org/10.1007/s43236-021-00299-5
  10. Karakose, E., Gencoglu, M.T., Karakose, M.: A new experimental approach using image processing-based tracking for an efficient fault diagnosis in pantograph-catenary systems. IEEE Trans. Industr. Inf. 13(2), 635-643 (2017) https://doi.org/10.1109/TII.2016.2628042
  11. Li, Y., Wei, X.: Pantograph Slide Plate Abrasion Detection Based on Deep Learning Network. In: Jia L., Qin Y., Suo J., Feng J., Diao L., An M. (eds) Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017. EITRT 2017. Lecture Notes in Electrical Engineering. vol 483. Springer, Singapore. https://doi.org/10.1007/978-981-10-7989-4_22
  12. Wang, Z., Oates, T.: Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Proceedings of the Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin. TX, USA, pp 25-26 (2015)
  13. Alom, M.Z., Hasan, M., Yakopcic, C.: Inception recurrent convolutional neural network for object recognition. Mach. Vis. Appl. 32, 28 (2021). https://doi.org/10.1007/s00138-020-01157-3
  14. Zhao, M., Zhong, S., Fu, X.: Deep residual shrinkage networks for fault diagnosis. IEEE Trans. Ind. Inform. 16, 4681-4690 (2020). https://doi.org/10.1109/TII.2019.2943898
  15. Yu, Y., Guo, L.H., Liu, Y.: Pareto-optimal adaptive loss residual shrinkage network for imbalanced fault diagnostics of machines. IEEE Trans. Industr. Inf. 18, 2233-2243 (2022). https://doi.org/10.1109/TII.2021.3094186
  16. Li, B., Luo, C., Wang, Z.: Application of GWO-SVM algorithm in arc detection of pantograph. IEEE Access. 8, 1-1 (2020) https://doi.org/10.1109/access.2019.2928059
  17. Xu, W., Su, Z., Cong, G.: (2020) High-Temperature Zone Localization Image Processing Algorithm for Pantograph Infrared Image. In: Liu B., Jia L., Qin Y., Liu Z., Diao L., An M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering. vol 640. Springer, Singapore. https://doi.org/10.1007/978-981-15-2914-6_6
  18. Jeong, J.Y., Kim, J.C., Kwak, S.: DC series arc diagnosis based on deep-learning algorithm with frequency-domain characteristics. J. Power Electron. 21, 1900-1909 (2021). https://doi.org/10.1007/s43236-021-00332-7
  19. Al Rahhal, M.M., Bazi, Y., Almubarak, H.: Dense convolutional networks with focal loss and image generation for electrocardiogram classification. IEEE Access. 7, 182225-182237 (2019). https://doi.org/10.1109/ACCESS.2019.2960116
  20. Ince, T., Kiranyaz, S., Eren, L.: Real-time motor fault detection by 1D Convolutional neural networks. IEEE Trans. Ind. Electron. 63(11), 7067-7075 (2016) https://doi.org/10.1109/TIE.2016.2582729
  21. Li, Z., Qu, N., Li, X.: Partial discharge detection of insulated conductors based on CNN-LSTM of attention mechanisms. J. Power Electron. 21, 1030-1040 (2021). https://doi.org/10.1007/s43236-021-00239-3