2017
DOI: 10.11591/ijeecs.v6.i3.pp628-637
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Comparative Study of Extension Mode Method in Reducing Border Distortion Effect for Transient Voltage Disturbance

Abstract: Wavelet transform is an essential method for preprocessing and analyzing non

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Cited by 7 publications
(7 citation statements)
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“…At the end of the signal, the convoluting window extends partially on the signal domain, in which abnormal coefficients arise and corrupted the transform. However, the distorted border gives significant influence that leads to situation of false indication [23][24][25][26]. Therefore, the threshold value was implemented for normal waveform to truncate the unwanted border distortion.…”
Section: Statistical Resultsmentioning
confidence: 99%
“…At the end of the signal, the convoluting window extends partially on the signal domain, in which abnormal coefficients arise and corrupted the transform. However, the distorted border gives significant influence that leads to situation of false indication [23][24][25][26]. Therefore, the threshold value was implemented for normal waveform to truncate the unwanted border distortion.…”
Section: Statistical Resultsmentioning
confidence: 99%
“…It has been employed to each level of decomposition process to get perfect reconstruction. Previously, S.Habsah et al [4] had employed extension mode method to reduce the border for non-stationary finite length signal and discovered that smooth padding yielded satisfying result. In this paper, three basic extension mode will be employed to identify its effectiveness on minimizing border distortion effect when sliding window signal is hired.…”
Section: Extension Mode Theorymentioning
confidence: 99%
“…The evolution of signal processing tools namely wavelet transform overcoming conventional Fourier analysis's drawback which can perfectly localize signal in frequency domain but failing in time domain [3]. The capability of wavelet in recognizing indexed in waveform make it well perform to extract features for gaining amplitude and frequency information [4]. It has the ability to classify types of power disturbances based on duration of harmonic content in the signal.…”
Section: Introductionmentioning
confidence: 99%
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