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Comparison of Classification Rate for PD Sources using Different Classification Schemes
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 Title & Authors
Comparison of Classification Rate for PD Sources using Different Classification Schemes
Park Seong-Hee; Lim Kee-Joe; Kang Seong-Hwa;
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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.
ANFIS;BP;Clustering;Classification;Partial discharge;PCA-LDA;
 Cited by
전력용 케이블 시편에서 전기트리 발생원에 따른 부분방전 분포 특성 및 발생원 분류기법 비교,박성희;정해은;임기조;강성화;

한국전기전자재료학회논문지, 2007. vol.20. 1, pp.57-64 crossref(new window)
Evaluation of Electrical Tree Degradation in Cross-Linked Polyethylene Cable Using Weibull Process of Propagation Time, Energies, 2017, 10, 11, 1789  crossref(new windwow)
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

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

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

F. H. Kreuger, E. Gulski and A. Krivda, 'Classification of Partial Discharge', IEEE Trans., El, Vol. 28, pp. 917-931, 1993 crossref(new window)

A. Mazrouna, M.M.A. Salama and R. Bartnikas, 'PD Pattern Recognition with Neural Networks', IEEE Trans., El, Vol. 25, pp. 917-931, 2002

J-S.R. Jang, C-T. Sun and E. Mitzutany, 'Neuro- Fuzzy and Soft Computering', Prentice-Hall International, Inc

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 crossref(new window)

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

F. H. Kreuger, Partial discharge Detection in High- Voltage Equipment, London, U.K: Butterworth, 1989

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

M. Turk and A. Pentland, 'Face recognition using eigenfaces', Proc. IEEE Conf on Computer Ksion and Pattern Recognition, pp. 586-591, 1991

R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd ed., John Wiley and Sons, Inc., 2002