Automatic identification of electric loads using switching transient current signals

Author

Thiruvaran, Tharmarajah ; Toan Phung ; Ambikairajah, E.

Author_Institution

Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia

fYear

2013

fDate

17-19 April 2013

Firstpage

252

Lastpage

256

Abstract

The automatic identification of different electric loads using the current waveform at the time of switching, is analysed in this paper. The time variation of the harmonics at the time of switching is modelled using the Hidden Markov Model with Gaussian Mixture Models representing the probabilities. Short Time Fourier Transform (STFT) and Wavelet Transform (WT) based features are compared at their optimum configurations. The STFT based feature gave an accuracy of 97.9% while the WT features provided an accuracy of 93.75% in a cross fold validation experiment.

Keywords

Fourier transforms; Gaussian distribution; hidden Markov models; load forecasting; wavelet transforms; Gaussian mixture models; STFT; automatic identification; current waveform; electric loads; hidden Markov model; short time Fourier transform; switching transient current signals; wavelet transform; Accuracy; Feature extraction; Harmonic analysis; Hidden Markov models; Switches; Time-frequency analysis; Transient analysis; Electric load identification; Hidden Markov Model; Wavelet Transform; harmonic analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON Spring Conference, 2013 IEEE

Conference_Location

Sydney, NSW

Print_ISBN

978-1-4673-6347-1

Type

conf

DOI

10.1109/TENCONSpring.2013.6584450

Filename

6584450