An investigative study into the sensitivity of different partial discharge φ-q-n pattern resolution sizes on statistical neural network pattern classification

Measurement: Journal of the International Measurement Confederation

Volumn 92, Issue , 2016, Pages 497-507

  Mas'ud, Abdullahi Abubakar ^a   Stewart, Brian G ^b   McMeekin, Scott G ^b  

^a JUBAIL INDUSTRIAL COLLEGE   (Saudi Arabia)

^b GLASGOW CALEDONIAN UNIVERSITY   (United Kingdom)

Author keywords

Classification; Partial discharge and ensemble neural network; Phase resolution and amplitude bin sizes

Indexed keywords

CLASSIFICATION (OF INFORMATION); STATISTICS;

BIN SIZE; DIFFERENT RESOLUTIONS; DISCHARGE PATTERNS; ENSEMBLE NEURAL NETWORK; PATTERN RESOLUTION; PHASE RESOLUTION; STATISTICAL NEURAL NETWORKS; TRAINING AND TESTING;

PARTIAL DISCHARGES;

EID: 84976892406     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2016.06.043     Document Type: Article

References (24)

1. [1] Gulski, E., Krivda, A., Neural network as a tool for recognition of partial discharges. IEEE Trans. Electr. Insul. 28:6 (1993), 984–1001.
2. [2] CIGRE, Working group report 21.03. Recognition of discharges. Electra 11 (1969), 61–98.
3. [3] Krivda, A., Automated recognition of partial discharges. IEEE Trans. Dielectr. Electr. Insul. 2:5 (1995), 792–821.
4. [4] Granadoa, J., Álvarez-Arroyo, C., Torralbaa, A., Rosendo-Macıas, J.A., Chávez, A., Burgos-Payánb, M., Time domain analysis of partial discharges envelope in medium voltage XLPE cables. Electr. Power Syst. Res. 125 (2015), 220–227.
5. [5] Albarracín, R., Ardila-Rey, J.A., Mas'ud, A. Abubakar, On the use of monopole antennas for determining the effect of the enclosure of a power transformer tank in partial discharges electromagnetic propagation. Sensors, 16(2), 2016, 148.
6. [6] Y. Tu, Z. Wang, P. Crossley, Partial discharge pattern recognition based on 2D wavelet transform and neural networks techniques, in: Power Engineering Society SUMMER Meetings vol. 1, 2002, pp. 411–416.
7. [7] Majidi, M., Fadali, M.S., Etezadi-Amoli, M., Oskuoee, M., Partial discharge pattern recognition via sparse representation and artificial neural network. IEEE Trans. Dielectr. Electr. Insul. 22:2 (2015), 1061–1070.
8. [8] Gulski, E., Krueger, F., Computer aided analysis of discharge patterns. J. Appl. Phys. 23 (1990), 1569–1575.
9. [9] Raymond, W.J.K., Illias, H.A., Bakar, A.H.A., Mokhlis, H., Partial discharge classifications: review of recent progress. Measurement 68 (2015), 164–181.
10. [10] Abdel Galil, T., Sharkawy, R., Salama, M., Partial discharge pattern classification using the fuzzy decision tree approach. IEEE Trans. Instrum. Meas. 54:6 (2005), 2258–2263.
11. [11] Babnik, T., Aggarwal, R., Moore, P., Data mining on a transformer partial discharge data using the self-organizing map. IEEE Trans. Dielectr. Electr. Insul. 14:2 (2007), 444–452.
12. [12] Khan, Y., Khan, A., Budimana, F.N., Beroual, A., Malika, N.H., Al-Arainya, A.A., Partial discharge pattern analysis using support vector machine to estimate size and position of metallic particle adhering to spacer in GIS. Electr. Power Syst. Res. 116 (2014), 391–398.
13. [13] Abdel-Galil, T.K., Hegazy, Y.G., Salama, M.M.A., Bartnikas, R., Partial discharge pulse pattern recognition using hidden Markov models. IEEE Trans. Dielectr. Electr. Insul. 11:4 (2004), 715–723.
14. [14] Karthikeyan, B., Gopal, S., Venkatesh, S., ART 2—an unsupervised neural network for PD pattern recognition and classification. Expert Syst. Appl. 31:2 (2006), 345–350.
15. [15] Mas'ud, A. Abubakar, Stewart, B.G., McMeekin, S.G., Application of Ensemble neural networks for classifying partial discharge patterns. Electr. Power Syst. Res. 110 (2014), 154–162.
16. [16] Kuo, C., Artificial identification system for transformer insulation aging. Expert Syst. Appl. 37:6 (2010), 4190–4197.
17. [17] A. Abubakar Mas'ud, B.G. Stewart, S.G. McMeekin, A. Nesbitt, Partial discharge pattern classification for angled point-oil-pressboard degradation, in: IEEE Conference of Electrical Insulation and Dielectric Phenomena, 2013, pp. 217–220.
18. [18] Trinh, N., Partial discharge XIX: discharges in air Part I: Physical mechanisms. IEEE Insul. Magaz. 11:2 (1999), 23–29.
19. [19] IEC 60270. British standard guide for partial discharge measurement; 2001.
20. [20] Maqsood, I., Khan, M., Abraham, A., An ensemble of NNs for weather forecasting. Neur. Comput. Appl. 13 (2004), 112–122.
21. [21] Jiminez, D., Dynamically weighted ensemble of neural network for classification. World Congr. Comput. Intell., 1998, 753–756.
22. [22] Cunningham, P., Carna, J., Jacob, S., Stability problems with the artificial neural networks and the ensemble solution. Artif. Intell. Med. 20:3 (2000), 217–225.
23. [23] Navone, H., Grannito, P., Verdes, P., Ceccato, H., A learning algorithm for NN ensemble. Artif. Intell. 12 (2001), 70–74.
24. [24] Majidi, M., Oskuoee, M., Improving pattern recognition accuracy of partial discharges by new data preprocessing methods. Electr. Power Syst. Res. 119 (2015), 100–110.