Sensitive Detection for High Impedance Fault on Transmission Line using Wavelet and Naive Bayes

Dimas Anton Asfani, I Made Yulistya Negara, Muhammad Rajavalens

Abstract

This paper presents the combination method of wavelet transform and naive bayes classifier to detect and classify the high impedance fault of transmision line. The fault’s current signal is transformed using wavelet. The transformed signal produces coefficients with certain pattern according to the type of fault that occurs. Then, coefficients of transformed signal are
variated become 7 variabels, based on the algorithm of classification. Those variabels are classified using naive bayes classifier to detect and classify the fault of transmission line. Three types of mother wavelet used in this study are Daubechies- 5 (Db5), Daubechies-8 (Db8), and Coiflet-5 (Coif5). Every mother wavelet produces different coefficients. However, they have similar pattern to the algorithm of classification. The highest accuracy of classification was obtained using coeffisients of Daubechies-5 (Db5) at 5th level. The classification accuracy is 97.09% using normal distribution, and 99.78% using kernel distribution.

Full Text:

PDF

References

M. Mirzaei, M.Z. A Ab Kadir, E. Moazami, H. Hizam "Review of Fault Location Methods for Distribution Power System". Australian Journal of Basic and Applied Sciences , 3(3): 2670-2676, 2009 ISSN 1991-8178

Liang J, Elangovan S, Devotta JBX. “A wavelet multiresolution analysis approach to fault detection and classification in transmission lines”. Electr Power Energ Sys 1998;20 (5):327–32.

M. Jayabharata Reddy, D.K. Mohanta “A wavelet-fuzzy combined approach for classification and location of transmission line faults” Electrical Power and Energy Systems 29 (2007) 669–678

Daman Suswanto, 2009 “Sistem Distribusi Tenaga Listrik”

James S. Walker “A Primer on WAVELETS and Their Scientific Applications” second edition 2008 pp41

Dimas Anton A, Adi Soeprijanto, Mauridhi Heri P, “Klasifikasi Gangguan Hubung Singkat pada Saluran Transmisi yang Dikompensasi Seri Menggunakan Kombinasi Wavelet dan ANFIS”. Seminar Nasional Efisiensi dan Konservasi Energi FISERGI, Semarang, 12 Desember 2005, hal. B158-B165

Aritz Pérez *, Pedro Larrañaga, Iñaki Inza “Bayesian classifiers based on kernel density estimation: Flexible classifiers” International Journal of Approximate Reasoning 50 (2009) 341–362

Refbacks

  • There are currently no refbacks.