Partial discharge recognition with a multi-resolution convolutional neural network

Sensors (Switzerland)

Volumn 18, Issue 10, 2018, Pages

  Li, Gaoyang ^a   Wang, Xiaohua ^a   Li, Xi ^b   Yang, Aijun ^a   Rong, Mingzhe ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b GLOBAL ENERGY INTERCONNECTION DEVELOPMENT AND COOPERATION ORGANIZATION   (China)

Author keywords

Convolutional neural network; Multi resolution analysis; Partial discharge; Ultra high frequency signals

Indexed keywords

CONVOLUTION; DEEP LEARNING; DEFECTS; ELECTRIC SWITCHGEAR; PARTIAL DISCHARGES;

CONVOLUTIONAL NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORKS (CNN); FULLY CONNECTED NEURAL NETWORK; GAS INSULATED SWITCHGEAR(GIS); MULTI-SENSOR INFORMATION; PARTIAL DISCHARGE RECOGNITION; ULTRA HIGH FREQUENCY (UHF); ULTRA HIGH FREQUENCY SIGNAL;

LONG SHORT-TERM MEMORY;

EID: 85055077813     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18103512     Document Type: Article

References (49)

1. Metwally, I.A. Technology progress in high-voltage gas-insulated substations. IEEE Potentials 2010, 29, 25–32. [CrossRef]
2. Riechert, U.; Holaus, W. Ultra high-voltage gas-insulated switchgear—A technology milestone. Eur. Trans. Electr. Power 2012, 22, 60–82. [CrossRef]
3. Schichler, U.; Koltunowicz, W.; Endo, F.; Feser, K.; Giboulet, A.; Girodet, A.; Hama, H.; Hampton, B.; Kranz, H.-G.; Lopez-Roldan, J. Risk assessment on defects in GIS based on PD diagnostics. IEEE Trans. Dielectr. Electr. Insul. 2013, 20, 2165–2172. [CrossRef]
4. Stone, G.C. Partial discharge diagnostics and electrical equipment insulation condition assessment. IEEE Trans. Dielectr. Electr. Insul. 2005, 12, 891–904. [CrossRef]
5. Bartnikas, R. Partial discharges. Their mechanism, detection and measurement. IEEE Trans. Dielectr. Electr. Insul. 2002, 9, 763–808. [CrossRef]
6. Wu, M.; Cao, H.; Cao, J.; Nguyen, H.L. An overview of state-of-the-art partial discharge analysis techniques for condition monitoring. IEEE Electr. Insul. Mag. 2015, 31, 22–35. [CrossRef]
7. Kurrer, R.; Feser, K. The application of ultra-high-frequency partial discharge measurements to gas-insulated substations. IEEE Trans. Power Deliv. 1998, 13, 777–782. [CrossRef]
8. High Voltage Test Techniques—Measurement of Partial Discharges by Electromagnetic and Acoustic Methods; IEC TS 62478:2016; BSI: London, UK, 2016.
9. Umamaheswari, R.; Sarathi, R. Identification of partial discharges in gas-insulated switchgear by ultra-high-frequency technique and classification by adopting multi-class support vector machines. Electr. Power Compon. Syst. 2011, 39, 1577–1595. [CrossRef]
10. Darabad, V.; Vakilian, M.; Blackburn, T.; Phung, B. An efficient PD data mining method for power transformer defect models using SOM technique. Int. J. Electr. Power Energy Syst. 2015, 71, 373–382. [CrossRef]
11. Mas’Ud, A.A.; Stewart, B.; McMeekin, S. Application of an ensemble neural network for classifying partial discharge patterns. Electr. Power Syst. Res. 2014, 110, 154–162. [CrossRef]
12. Evagorou, D.; Kyprianou, A.; Lewin, P.; Stavrou, A.; Efthymiou, V.; Metaxas, A.; Georghiou, G. Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network. IET Sci. Meas. Technol. 2010, 4, 177–192. [CrossRef]
13. Wang, K.; Li, J.; Zhang, S.; Liao, R.; Wu, F.; Yang, L.; Li, J.; Grzybowski, S.; Yan, J. A hybrid algorithm based on s transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 1042–1060. [CrossRef]
14. Zhu, M.-X.; Xue, J.-Y.; Zhang, J.-N.; Li, Y.; Deng, J.-B.; Mu, H.-B.; Zhang, G.-J.; Shao, X.-J.; Liu, X.-W. Classification and separation of partial discharge ultra-high-frequency signals in a 252 kV gas insulated substation by using cumulative energy technique. IET Sci. Meas. Technol. 2016, 10, 316–326. [CrossRef]
15. Li, L.; Tang, J.; Liu, Y. Partial discharge recognition in gas insulated switchgear based on multi-information fusion. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 1080–1087. [CrossRef]
16. Albarracín, R.; Robles, G.; Martinez-Tarifa, J.M.; Ardila-Rey, J. Separation of sources in radiofrequency measurements of partial discharges using time–power ratio maps. ISA Trans. 2015, 58, 389–397. [CrossRef] [PubMed]
17. Li, J.; Jiang, T.; Harrison, R.F.; Grzybowski, S. Recognition of ultra high frequency partial discharge signals using multi-scale features. IEEE Trans. Dielectr. Electr. Insul. 2012, 19, 1412–1420. [CrossRef]
18. Wang, X.; Li, X.; Rong, M.; Xie, D.; Ding, D.; Wang, Z. UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology. Sensors 2017, 17, 177. [CrossRef] [PubMed]
19. Gu, F.-C.; Chang, H.-C.; Chen, F.-H.; Kuo, C.-C.; Hsu, C.-H. Application of the Hilbert-Huang transform with fractal feature enhancement on partial discharge recognition of power cable joints. IET Sci. Meas. Technol. 2012, 6, 440–448. [CrossRef]
20. Dai, D.; Wang, X.; Long, J.; Tian, M.; Zhu, G.; Zhang, J. Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition. IET Sci. Meas. Technol. 2016, 11, 186–193. [CrossRef]
21. Majidi, M.; Fadali, M.S.; Etezadi-Amoli, M.; Oskuoee, M. Partial discharge pattern recognition via sparse representation and ANN. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 1061–1070. [CrossRef]
22. Khan, Y. Partial discharge pattern analysis using PCA and back-propagation artificial neural network for the estimation of size and position of metallic particle adhering to spacer in GIS. Electr. Eng. 2016, 98, 29–42. [CrossRef]
23. Li, X.; Wang, X.; Yang, A.; Xie, D.; Ding, D.; Rong, M. Propogation characteristics of PD-induced UHF signal in 126 kV GIS with three-phase construction based on time–frequency analysis. IET Sci. Meas. Technol. 2016, 10, 805–812. [CrossRef]
24. Auger, F.; Flandrin, P.; Gonçalvès, P.; Lemoine, O. Time-Frequency Toolbox; CNRS France: Paris, France; Rice University: Houston, TX, USA, 1996.
25. Zhang, S.; Li, C.; Wang, K.; Li, J.; Liao, R.; Zhou, T.; Zhang, Y. Improving recognition accuracy of partial discharge patterns by image-oriented feature extraction and selection technique. IEEE Trans. Dielectr. Electr. Insul. 2016, 23, 1076–1087. [CrossRef]
26. Wang, K.; Liao, R.; Yang, L.; Li, J.; Grzybowski, S.; Hao, J. Optimal features selected by NSGA-II for partial discharge pulses separation based on time-frequency representation and matrix decomposition. IEEE Trans. Dielectr. Electr. Insul. 2013, 20, 825–838. [CrossRef]
27. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436. [CrossRef] [PubMed]
28. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. In Proceedings of the Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–8 December 2012; pp. 1097–1105.
29. Li, G.; Rong, M.; Wang, X.; Li, X.; Li, Y. Partial discharge patterns recognition with deep Convolutional Neural Networks. In Proceedings of the Condition Monitoring and Diagnosis, Xi’an, China, 25–28 September 2016; pp. 324–327.
30. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 770–778.
31. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1–9.
32. Chollet, F. Xception: Deep Learning With Depthwise Separable Convolutions. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 1251–1258.
33. Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 5987–5995.
34. Shao, L.; Wu, D.; Li, X. Learning Deep and Wide: A Spectral Method for Learning Deep Networks. IEEE Trans. Neural Netw. Learn. Syst. 2017, 25, 2303–2308. [CrossRef] [PubMed]
35. Dieleman, S.; Schrauwen, B. Multiscale approaches to music audio feature learning. In Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR-2013), Pontifícia Universidade Católica do Paraná, Curitiba, Brazil, 4–8 November 2013; pp. 116–121.
36. Mathieu, M.; Couprie, C.; LeCun, Y. Deep multi-scale video prediction beyond mean square error. arXiv Preprint 2015, arXiv:1511.05440.
37. Takahashi, N.; Mitsufuji, Y. Multi-Scale multi-band densenets for audio source separation. In Proceedings of the 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, 15–18 October 2017; pp. 21–25.
38. Tang, Y.; Mohamed, A.-R. Multiresolution deep belief networks. In Proceedings of the Artificial Intelligence and Statistics, La Palma, Canary Islands, Spain, 21–23 April 2012; pp. 1203–1211.
39. He, K.; Zhang, X.; Ren, S.; Sun, J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 37, 1904–1916. [CrossRef] [PubMed]
40. Greff, K.; Srivastava, R.K.; Koutník, J.; Steunebrink, B.R.; Schmidhuber, J. LSTM: A Search Space Odyssey. IEEE Trans. Neural Netwo. Learn. Syst. 2015, 28, 2222–2232. [CrossRef] [PubMed]
41. Li, X.; Wang, X.; Xie, D.; Wang, X.; Yang, A.; Rong, M. Time–frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments. IET Sci. Meas. Technol. 2018, 12, 169–175. [CrossRef]
42. Mikolov, T.; Karafiát, M.; Burget, L.; Cernocký, J.; Khudanpur, S. Recurrent neural network based language model. In Proceedings of the INTERSPEECH 2010—11th Annual of Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, 26–30 September 2010; pp. 1045–1048.
43. Pan, S.J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359. [CrossRef]
44. Weston, J.; Ratle, F.; Mobahi, H.; Collobert, R. Deep learning via semi-supervised embedding. In Neural Networks: Tricks of the Trade; Springer: Berlin/Heidelberg, Germany, 2012; pp. 639–655.
45. Bengio, Y.; Paiement, J.F.; Vincent, P.; Delalleau, O.; Roux, N.L.; Ouimet, M. Out-of-sample extensions for LLE, IsoMap, MDS, Eigenmaps, and Spectral Clustering. Adv. Neural Inf. Process. Syst. 2004, 16, 177–184.
46. Williams, C.K.I. On a Connection between Kernel PCA and Metric Multidimensional Scaling. Mach. Learn. 2002, 46, 11–19. [CrossRef]
47. Chopra, S.; Hadsell, R.; Lecun, Y. Learning a Similarity Metric Discriminatively, with Application to Face Verification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20–25 June 2005; pp. 539–546.
48. Li, T.; Rong, M.; Zheng, C.; Wang, X. Development simulation and experiment study on UHF partial discharge sensor in GIS. IEEE Trans. Dielectr. Electr. Insul. 2012, 19, 1421–1430. [CrossRef]
49. Erhan, D.; Bengio, Y.; Courville, A.; Vincent, P. Visualizing Higher-Layer Features of a Deep Network; Technical Report 1341; University of Montreal: Montreal, QC, Canada, 2009; pp. 1–13.