Detection and classification of impulse faults in transformer using wavelet transform and artificial neural network

Author

Vanamadevi, N. ; Arivamudhan, M. ; Santhi, S.

Author_Institution

Dept. of Instrum. Eng., Annamalai Univ., Annamalainagar

fYear

2008

fDate

24-27 Nov. 2008

Firstpage

72

Lastpage

76

Abstract

This paper aims at describing a method for the detection and classification of impulse faults in a transformer winding using wavelet transform and an artificial neural network. The method is explained by considering the lumped parameter model of a winding. The WT decomposes the signal and RMS value of the detailed signal is extracted to train the ANN. The simulation results are satisfactory in detection and classification of faults.

Keywords

fault diagnosis; neural nets; power engineering computing; power transformers; wavelet transforms; artificial neural network; impulse fault classification; impulse fault detection; lumped parameter model; transformer winding; wavelet transform; Artificial neural networks; Capacitance; Circuit faults; Electronic mail; Fault detection; Frequency domain analysis; Impulse testing; Instruments; Wavelet transforms; Windings; Impulse faults; Transformer; Wavelet transform ANN;

fLanguage

English

Publisher

ieee

Conference_Titel

Sustainable Energy Technologies, 2008. ICSET 2008. IEEE International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-1887-9

Electronic_ISBN

978-1-4244-1888-6

Type

conf

DOI

10.1109/ICSET.2008.4746975

Filename

4746975