A novel application of deep belief networks in learning partial discharge patterns for classifying corona, surface, and internal discharges

IEEE Transactions on Industrial Electronics

Volumn 67, Issue 4, 2020, Pages 3277-3287

  Karimi, Masoud ^a   Majidi, Mehrdad ^b   Mirsaeedi, Hamed ^c   Arefi, Mohammad Mehdi ^d   Oskuoee, Mohammad ^e  

^a SHAHID BEHESHTI UNIVERSITY   (Iran)

^b Reno   (United States)

^c UNIVERSITY OF TEHRAN   (Iran)

^d SHIRAZ UNIVERSITY   (Iran)

^e NIROO RESEARCH INSTITUTE   (Iran)

Author keywords

Deep belief networks (DBNs); partial discharge (PD); pattern recognition; statistical moments; vector norms

Indexed keywords

BAYESIAN NETWORKS; DATA MINING; EXTRACTION; FEATURE EXTRACTION; FUZZY INFERENCE; FUZZY LOGIC; FUZZY NEURAL NETWORKS; PARTIAL DISCHARGES; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; SURFACE DISCHARGES;

DEEP BELIEF NETWORK (DBN); DEEP BELIEF NETWORKS; EXTRACTING FEATURES; FEATURE EXTRACTION TECHNIQUES; NEURAL NETWORKS , FUZZY LOGIC; PARTIAL DISCHARGE PATTERN; STATISTICAL MOMENTS; VECTOR NORM;

DEEP LEARNING;

EID: 85076638401     PISSN: 02780046     EISSN: 15579948     Source Type: Journal    
DOI: 10.1109/TIE.2019.2908580     Document Type: Article

References (43)

1. L. Satish and B. Gururaj, "Partial discharge pattern classification using multilayer neural networks, " IEE Proc. A Sci., Meas. Technol., vol. 140, no. 4, pp. 323-330, Jul. 1993.
2. G. Montanari, A. Contin, and A. Cavallini, "Random sampling and data processing for PD-pulse height and shape analysis, " IEEE Trans. Dielect. Elect. Insul., vol. 7, no. 1, pp. 30-39, Feb. 2000.
3. A. Contin, A. Cavallini, G. Montanari, G. Pasini, and F. Puletti, "Digital detection and fuzzy classification of partial discharge signals, " IEEE Trans. Dielect. Elect. Insul., vol. 9, no. 3, pp. 335-348, Jun. 2002.
4. N. Sahoo, M. Salama, and R. Bartnikas, "Trends in partial discharge pattern classification:Asurvey, " IEEE Trans. Dielect. Elect. Insul., vol. 12, no. 2, pp. 248-264, Apr. 2005.
5. A. Krivda, "Automated recognition of partial discharges, " IEEE Trans. Dielect. Elect. Insul., vol. 2, no. 5, pp. 796-821, Oct. 1995.
6. R. James and B. Phung, "Development of computer-based measurements and their application to PD pattern analysis, " IEEE Trans. Dielect. Elect. Insul., vol. 2, no. 5, pp. 838-856, Oct. 1995.
7. E. Gulski, "Digital analysis of partial discharges, " IEEE Trans. Dielect. Elect. Insul., vol. 2, no. 5, pp. 822-837, Oct. 1995.
8. E. Gulski and A. Krivda, "Neural networks as a tool for recognition of partial discharges, " IEEE Trans. Elect. Insul., vol. 28, no. 6, pp. 984-1001, Dec. 1993.
9. R. Bartnikas, "Partial discharges. Their mechanism, detection and measurement, " IEEE Trans. Dielect. Elect. Insul., vol. 9, no. 5, pp. 763-808, Oct. 2002.
10. F. Kreuger, E. Gulski, and A. Krivda, "Classification of partial discharges, " IEEE Trans. Elect. Insul., vol. 28, no. 6, pp. 917-931, Dec. 1993.
11. E. Gulski and F. Kreuger, "Computer-aided recognition of discharge sources, " IEEE Trans. Elect. Insul., vol. 27, no. 1, pp. 82-92, Feb. 1992.
12. L. Hao and P. Lewin, "Partial discharge source discrimination using a support vector machine, " IEEE Trans. Dielect. Elect. Insul., vol. 17, no. 1, pp. 189-197, Feb. 2010.
13. R. Ambikairajah, B. T. Phung, J. Ravishankar, and T. Blackburn, "Spectral features for the classification of partial discharge signals from selected insulation defect models, " IET Sci., Meas. Technol., vol. 7, pp. 104-111, 2013.
14. A. A. Mas'ud, B. G. Stewart, and S. G. McMeekin, "An investigative study into the sensitivity of different partial discharge-q-n pattern resolution sizes on statistical neural network pattern classification, " Measurement, vol. 92, pp. 497-507, 2016.
15. F. Gomes, T. Oliveira, F. Brazil, A. Pamplona, V. Farias, and A. Silveira, "A method for the behavioral analysis of partial discharges in hydrogenerators by generalized linear models, " Elect. Power Syst. Res., vol. 140, pp. 284-287, 2016.
16. Y. Zhu, Y. Jia, and L. Wang, "Partial discharge pattern recognition method based on variable predictive model-based class discriminate and partial least squares regression, " IET Sci., Meas. Technol., vol. 10, pp. 737-744, 2016.
17. M. Majidi and M. Oskuoee, "Improving pattern recognition accuracy of partial discharges by new data preprocessing methods, " Elect. Power Syst. Res., vol. 119, pp. 100-110, 2015.
18. Y. Han and Y. Song, "Using improved self-organizing map for partial discharge diagnosis of large turbogenerators, " IEEE Trans. Energy Convers., vol. 18, no. 3, pp. 392-399, Sep. 2003.
19. M. Majidi, M. S. Fadali, M. Etezadi-Amoli, and M. Oskuoee, "Partial discharge pattern recognition via sparse representation and ANN, " IEEE Trans. Dielect. Elect. Insul., vol. 22, no. 2, pp. 1061-1070, Apr. 2015.
20. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
21. Y. Bengio andY. LeCun, "Scaling learning algorithms towardsAI, " Large-Scale Kernel Mach., vol. 34, pp. 1-41, 2007.
22. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, pp. 504-507, 2006.
23. J. Tang, M. Jin, F. Zeng, X. Zhang, and R. Huang, "Assessment of PD severity in gas-insulated switchgear with an SSAE, " IET Sci., Meas. Technol., vol. 11, pp. 423-430, 2017.
24. H. Zhang, T. Blackburn, B. Phung, andD. Sen, "Anovelwavelet transform technique for on-line partial discharge measurements. 1. WT denoising algorithm, " IEEE Trans. Dielect. Elect. Insul., vol. 14, no. 1, pp. 3-14, Feb. 2007.
25. L. Satish and B. Nazneen, "Wavelet-based denoising of partial discharge signals buried in excessive noise and interference, " IEEE Trans. Dielect. Elect. Insul., vol. 10, no. 2, pp. 354-367, Apr. 2003.
26. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layerwise training of deep networks, " Adv. Neural Inf. Process. Syst., vol. 19, pp. 153-160, 2007.
27. F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, " Mech. Syst. Signal Process., vol. 72, pp. 303-315, 2016.
28. Z. Chen, X. Zeng, W. Li, and G. Liao, "Machine fault classification using deep belief network, " in Proc. IEEE Int. Instrum. Meas. Technol. Conf., 2016, pp. 1-6.
29. D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, "A learning algorithm for Boltzmann machines, " Cogn. Sci., vol. 9, pp. 147-169, 1985.
30. Y. Chen, X. Zhao, and X. Jia, "Spectral-spatial classification of hyperspectral data based on deep belief network, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2381-2392, Jun. 2015.
31. R. Salakhutdinov and G. Hinton, "Deep Boltzmann machines, " in Proc. Int. Conf. Artif. Intell. Statist., 2009, pp. 448-455.
32. R. Salakhutdinov, "Learning deep generative models, " Ann. ReviewStatist. Appl., vol. 2, pp. 361-385, 2015.
33. G. E. Hinton, "Training products of experts by minimizing contrastive divergence, " Neural Comput., vol. 14, pp. 1771-1800, 2002.
34. H. Lee, A. H.-S. Ng, D. Koller, and K. V. Shenoy, "Unsupervised feature learning via sparse hierarchical representations, " Ph. D. dissertation, Stanford Univ., Stanford, CA, USA, 2010.
35. G. E. Hinton, "A practical guide to training restricted Boltzmann machines, " in Neural Networks: Tricks of the Trade. Berlin, Germany: Springer, 2012, pp. 599-619.
36. S. Escalera, O. Pujol, and P. Radeva, "Separability of ternary codes for sparse designs of error-correcting output codes, " Pattern Recognit. Lett., vol. 30, pp. 285-297, 2009.
37. W. J. K. Raymond andH. A. Illias, "High noise tolerance feature extraction for partial discharge classification in XLPE cable joints, " IEEE Trans. Dielect. Elect. Insul., vol. 24, no. 1, pp. 66-74, Feb. 2017.
38. X. Li, G. Cui, and Y. Dong, "Graph regularized non-negative low-rank matrix factorization for image clustering, " IEEE Trans. Cybern., vol. 47, no. 11, 3840-3853, Nov. 2017.
39. Y. Dong, D. Tao, and X. Li, "Nonnegative multiresolution representationbased texture image classification, " ACM Trans. Intell. Syst. Technol., vol. 7, pp. 1-21, 2015, Art. no. 4.
40. X. Li, G. Cui, and Y. Dong, "Refined-graph regularization-based nonnegative matrix factorization, " ACM Trans. Intell. Syst. Technol, vol. 9, pp. 1-21, 2017, Art. no. 1.
41. X. Li, Q. Lu, Y. Dong, and D. Tao, "SCE: A manifold regularized setcovering method for data partitioning, " IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 5, pp. 1760-1773, May 2018.
42. Y. Dong, D. Tao, X. Li, J. Ma, and J. Pu, "Texture classification and retrieval using shearlets and linear regression, " IEEE Trans. Cybern., vol. 45, no. 3, pp. 358-369, Mar. 2015.
43. C. Gong, H. Zhang, J. Yang, and D. Tao, "Learning with inadequate and incorrect supervision, " in Proc. IEEE Int. Conf. Data Mining, 2017, pp. 889-894.