An ensemble-boosting algorithm for classifying partial discharge defects in electrical assets

Machines

Volumn 5, Issue 3, 2017, Pages

  Mas'ud, Abdullahi Abubakar ^a   Ardila Rey, Jorge Alfredo ^b   Albarracín, Ricardo ^c   Muhammad Sukki, Firdaus ^d  

^a JUBAIL INDUSTRIAL COLLEGE   (Saudi Arabia)

^b DEPARTAMENTO DE FÍSICA   (Chile)

^c School of Industrial Engineering   (Spain)

^d ROBERT GORDON UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural networks; Condition monitoring; Electrical assets; Ensemble boosting algorithm; Insulation diagnosis; Partial discharge; Single artificial neural network

Indexed keywords

EID: 85034224350     PISSN: None     EISSN: 20751702     Source Type: Journal    
DOI: 10.3390/machines5030018     Document Type: Article

References (26)

1. Barré, O.; Napame, B. The Insulation for Machines Having a High Lifespan Expectancy, Design, Tests and Acceptance Criteria Issures. Machines 2017, 5, 7
2. Haque, S.M.; Ardila-Rey, J.A.; Masud, A.A.; Umar, Y.; Albarracín, R. Electrical Properties of Different Polymeric Materials and their Applications: The Influence of Electric Field. In Properties and Applications of Polymer Dielectrics; Boxue, D., Ed.; InTech: Rijeka, Croatia, 2017; Chapter 3
3. Jardine, A.K.; Lin, D.; Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 2006, 20, 1483-1510
4. Gill, P. Electrical Power Equipment Maintenance and Testing, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2008
5. Kreuger, F.H. Partial Discharge Detection in High-Voltage Equipment; Butterworths: London, UK, 1989
6. Boggs, S. Partial discharge. III. Cavity-induced PD in solid dielectrics. IEEE Electr. Insul. Mag. 1990, 6, 11-16
7. Cavallini, A.; Montanari, G.C.; Contin, A.; Pulletti, F. A new approach to the diagnosis of solid insulation systems based on PD signal inference. IEEE Electr. Insul. Mag. 2003, 19, 23-30
8. Stone, G.C.; Sedding, H.G.; Costello, M.J. Application of partial discharge testing to motor and generator stator winding maintenance. IEEE Trans. Ind. Appl. 1996, 32, 459-464
9. Asiri, Y.; Vouk, A.; Renforth, L.; Clark, D.; NeuralWare, J.C. Neural network based classification of partial discharge in HV motors. In Proceedings of the 2011 Electrical Insulation Conference (EIC), Annapolis, MD, USA, 5-8 June 2011; pp. 333-339
10. Markalous, S.M.; Tenbohlen, S.; Feser, K. Detection and location of partial discharges in power transformers using acoustic and electromagnetic signals. IEEE Trans. Dielectr. Electr. Insul. 2008, 15, 1576-1583
11. Albarracín, R.; Ardila-Rey, J.A.; Masud, A.A. On the Use of Monopole Antennas for Determining the Effect of the Enclosure of a Power Transformer Tank in Partial Discharges Electromagnetic Propagation. Sensors 2016, 16, 148. [CrossRef] [PubMed]
12. Gulski, E.; Krivda, A. Neural networks as a tool for recognition of partial discharges. IEEE Trans. Electr. Insul. 1993, 28, 984-1001
13. Gulski, E. Computer-Aided Recognition of Partial Discharges Using Statistical Tools; Delft University: Delft, The Netherlands, 1991
14. Masud, A.A.; Albarracín, R.; Ardila-Rey, J.A.; Muhammad-Sukki, F.; Illias, H.A.; Bani, N.A.; Munir, A.B. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions. Energies 2016, 9, 574
15. Lai, K.X.; Phung, B.T.; Blackburn, T.R. Application of data mining on partial discharge Part I: Predictive modelling classification. IEEE Trans. Dielectr. Electr. Insul. 2010, 17, 846-854
16. Hao, L.; Lewin, P.L. Partial discharge source discrimination using a support vector machine. IEEE Trans. Dielectr. Electr. Insul. 2010, 17, 189-197
17. Ma, H.; Chan, J.C.; Saha, T.K.; Ekanayake, C. Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources. IEEE Trans. Dielectr. Electr. Insul. 2013, 20, 468-478
18. Günter, S.; Bunke, H. New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task; International Workshop on Multiple Classifier Systems; Springer: Berlin/Heidelberg, Germany, 2003; pp. 326-335
19. Ivanov, Y.; Hamid, R. Weighted ensemble boosting for robust activity recognition in video. Mach. Graph. Vis. 2006, 15, 415
20. IEC-60270. High-Voltage Test Techniques-Partial Discharge Measurements, 3rd ed.; International Electrotechnical Commission (IEC): New Delhi, India, 2000
21. Opitz, D.; Maclin, R. Popular ensemble methods: An Empirical study. J. Artif. Intell. Res. 1999, 11, 169-198
22. Brieman, L. Bagging Predictors. Mach. Learn. 1996, 24, 123-124
23. Schapire, R.E. The Strength ofWeak Learnability. Mach. Learn. 1990, 5, 197-227
24. Haykin, S.S. Neural Networks: A Comprehensive Foundation; Prentice Hall: Upper Saddle River, NJ, USA, 1998
25. Giao, T.N. Partial discharge XIX: Discharge in air part I: Physical mechanisms. IEEE Electr. Insul. Mag. 1995, 11, 23-29
26. Masud, A.A.; Stewart, B.G.; McMeekin, S.G. Application of an ensemble neural network classifying partial discharge patterns. Electr. Power Syst. Res. 2014, 110, 154-162