Data Compression of Power Quality Events Using the Slantlet Transform

The slantlet transform (SLT) is an orthogonal discrete wavelet transform (DWT) with two zero moments and with improved time localization. It also retains the basic characteristic of the usual filterbank such as octave band characteristic, a scale dilation factor of two and efficient implementation. However, the SLT is based on the principle of designing different filters for different scales unlike iterated filterbank approaches for the DWT. In this paper a novel approach for power quality data compression using the SLT is presented and its performance in terms of compression ratio (CR), percentage of energy retained and mean square error present in the reconstructed signals is assessed. Varieties of power quality events, which include voltage sag, swell, momentary interruption, harmonics, transient oscillation, and voltage flicker are used to test the performance of the new approach. Computer simulation results indicate that the SLT offers superior compression performance compared to the conventional DCT and the DWT based approaches.