Abstract

Wavelets are used to analyze the frequency components of a signal according to a scale. Now-a-days, wavelet\ntransforms have been studied and applied effectively in science and technology fields. One such area of applications of digital\nsignal processing tools is ‘Power Quality’. Of late, Slantlet transform (SLT) can be developed by considering the lengths of\nthe discrete time basis function and their moments to achieve both time localization and smoothness properties. It is an\northogonal DWT with two zero moments and possesses improved time localization properties. Present paper describes SLT\nused for compression of different power quality disturbance data. The performance of proposed novel technique called\nslantlet transform (SLT) can be assessed by in terms of compression ratio, mean square error and percentage of energy\nretained in the reconstructed signal is assessed. Simulated data related to varieties of power quality events which include sine\nimpulse, voltage sag, voltage swell, harmonics, momentary interruption, voltage flicker and transient oscillation are used to\ntest the performance of SLT based approach for data compression and signal reconstruction. MATLAB based simulation\nresults shows that the SLT offers superior compression performance results compared to the conventional discrete wavelet\ntransform and discrete cosine transform based approaches reported in the literature.