[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5370/JEET.2006.1.2.257

Comparison of Classification Rate for PD Sources using Different Classification Schemes

Park Seong-Hee (Graduate school of Electrical Engineering, Chungbuk National University)
Lim Kee-Joe (School of Electrical and Computer Engineering, Chungbuk National University)
Kang Seong-Hwa (Dept. of Fire Prevention Engineering, Chungcheong University)

Publication Information

Journal of Electrical Engineering and Technology / v.1, no.2, 2006 , pp. 257-262 More about this Journal

Abstract

Insulation failure in an electrical utility depends on the continuous stress imposed upon it. Monitoring of the insulation condition is a significant issue for safe operation of the electrical power system. In this paper, comparison of recognition rate variable classification scheme of PD (partial discharge) sources that occur within an electrical utility are studied. To acquire PD data, five defective models are made, that is, air discharge, void discharge and three types of treeinging discharge. Furthermore, these statistical distributions are applied to classify PD sources as the input data for the classification tools. ANFIS shows the highest rate, the value of which is 99% and PCA-LDA and ANFIS are superior to BP in regards to other matters.

Keywords

ANFIS; BP; Clustering; Classification; Partial discharge; PCA-LDA;

Citations & Related Records

Reference

1	S. Boggs and J. Densly, 'Fundamentals of PD in the context of field cable resting', IEEE Insulation Magazine, Vol. 16, No. 5, pp. 13-18, 2000
2	M. Turk and A. Pentland, 'Face recognition using eigenfaces', Proc. IEEE Conf on Computer Ksion and Pattern Recognition, pp. 586-591, 1991
3	R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd ed., John Wiley and Sons, Inc., 2002
4	Kai Gao and Chengqi Wu, 'PD Pattern Recognition for Stator Bar Models with Six Kinds of Characteristic Vectors Using BP Network'. IEEE Trans. EI, Vol. 9, No. 3, pp. 381-388, 2002
5	Jyh-Shing Roger Jang, 'ANFIS: Adaptive-Network- Based Fuzzy Inference System', IEEE Trans. On system, Vol. 23, No. 3, pp. 665-675, May/June, 1993
6	Witold Pedrycz, 'Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Network', IEEE Trans. on neural network, Vol. 9, 99601 - 605, July 1998
7	F. H. Kreuger, E. Gulski and A. Krivda, 'Classification of Partial Discharge', IEEE Trans., El, Vol. 28, pp. 917-931, 1993 DOI ScienceOn
8	A. Mazrouna, M.M.A. Salama and R. Bartnikas, 'PD Pattern Recognition with Neural Networks', IEEE Trans., El, Vol. 25, pp. 917-931, 2002
9	J-S.R. Jang, C-T. Sun and E. Mitzutany, 'Neuro- Fuzzy and Soft Computering', Prentice-Hall International, Inc
10	E. Gulski and A. Krivda, 'Neural Networks as a Tool for Recognition of Partial Discharges', IEEE Trans., El, Vol. 28, No. 6, pp. 984-1002, 1993 DOI ScienceOn
11	A. Cavallini and G.-C. Montanari, A. Contin and F. Puletti, 'A new approach to the Diagnosis of Solid Insulation Systems Based on PD Signal Inference', IEEE DEIS, Vol. 19, No. 2, pp. 23-28, 2003
12	F. H. Kreuger, Partial discharge Detection in High- Voltage Equipment, London, U.K: Butterworth, 1989