Comparison of two partial discharge classification methods

Conference Record of IEEE International Symposium on Electrical Insulation

Volumn , Issue , 2010, Pages

  Hunter, J A ^a   Hao, L ^a   Lewin, P L ^a   Evagorou, D ^b   Kyprianou, A ^b   Georghiou, G E ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b UNIVERSITY OF CYPRUS   (Cyprus)

Author keywords

Partial discharge; Probabilistic neural network; Probability density function; Support vector machines; Wavelet analysis

Indexed keywords

AUTO-TRANSFORMERS; BUSHING TAP; CLASSIFICATION PERFORMANCE; CORONA DISCHARGES; FEATURE EXTRACTION ALGORITHMS; FEATURE VECTORS; HIGH FREQUENCY CURRENT TRANSFORMERS; HIGH VOLTAGE; IDENTIFICATION RATES; PARTIAL DISCHARGE CLASSIFICATION; PARTIAL DISCHARGE IDENTIFICATIONS; POWER CYCLE; PROBABILISTIC NEURAL NETWORKS; PROBABILITY DENSITIES; SENSING EQUIPMENTS; SIGNAL CLASSIFICATION METHODS; SIGNAL PROCESSING ALGORITHMS; STANDARD DEVIATION; SUPPORT VECTOR; TWO MACHINES; WAVELET PACKET ANALYSIS; WAVELET PACKET COEFFICIENT;

BUSHINGS; ELECTRIC CORONA; ELECTRIC TRANSFORMERS; FEATURE EXTRACTION; GEARS; IMAGE RETRIEVAL; NEURAL NETWORKS; PARTIAL DISCHARGES; PROBABILITY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; SURFACE DISCHARGES; TECHNICAL PRESENTATIONS; VECTORS; WAVELET ANALYSIS;

PROBABILITY DENSITY FUNCTION;

EID: 77957361039     PISSN: 01642006     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ELINSL.2010.5549736     Document Type: Conference Paper

References (14)

1. Partial Discharge Measurements, IEC Publication 60270, 2000.
2. S. Boggs and J. Densley, "Fundamentals of partial discharge in the context of field cable testing," IEEE Electric. Insul. Mag., vol. 16, pp 13- 18, September-October 2000.
3. L. Hao, P. L. Lewin and S. Dodd, "Comparison of support vector machine based partial discharge identification parameters," In Conference record of the 2006 IEEE International Symposium on Electrical Insulation. pp 110-113, June 2006.
4. E. Gulski and F. H. Kreuger, "Computer-aided recognition of partial discharge sources," IEEE Trans. on Electric. Insul., vol. 27, pp. 82-92, February 1992.
5. J. P. van Bolhuis, E. Gulski and J. J. Smit, "Monitoring and diagnostic of transformer insulation," IEEE Trans. on Power Delivery, vol. 17, pp. 528-536, 2002.
6. F. H. Kreuger, E. Gulski and A. Krivda, "Classification of partial discharges," IEEE Trans. on Electric. Insul., vol. 28, pp. 917-931, December 1993.
7. M. Hoof and R. Patsch, "Investigation of PD resulting from transformers using the PSA method," in IEEE 1997 Annual report on Electric. Insul. and Dielectric Phenomena, vol. 2, pp. 562-566, October 1997.
8. Y. Tian, P. L. Lewin, A. E. Davies, S. J. Sutton and S. G. Swingler, "Application of acoustic emission techniques and artificial neural networks to partil discharge classification," In Conference Record of the 2002 IEEE International Symposium on Electric. Insul., pp. 119- 123, April 2002.
9. N. F. Ab Aziz, L. Hao and P. L. Lewin, "Analysis of partial discharge measurement data using a support vector machine," 5th Student Conference on Research and Development, pp. 1-6, December 2007.
10. X. D. Ma, C. Zhou and I. J. Kemp, "DSP based partial discharge characterisation by wavelet analysis," ISDEIV 2000, Vol 2, pp. 780-783, September 2000.
11. L. Hao, P. L. Lewin and S. G. Swingler, "Identification of multiple partial discharge sources," Proc. from the International Conference on Condition monitoring and Diagnosis, pp. 118-121, April 2008.
12. G. C. Stone, B. A. Lloyd, S.R. Campbell and H. G. Sedding, "Development of automatic, continuous partial discharge monitoring systems to detect motor and generator partial discharges," Proc. of the IEEE Electrical Machines and Drives Conference, pMA2-3.1-pMA2-3.3, 1997.
13. D. K. Kim and N. S. Kim, "Rapid speaker adaptation using probabilistic principal component analysis," IEEE Trans. on Signal Processing, Vol. 8, pp. 180-183, 2001.
14. D. Evagorou, A. Kyprianou, P.L. Lewin, A. Stavrou, V. Efthymiou, A. C. Metaxas G. E. Georghiou, "Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network," IET Science, Measurement and Technology, (accepted for publication).