Analysis of Wavelet Transform applied to the segmentation of disturbance signals with different sampling rates

The segmentation of disturbance signals obtained by power quality meters at distribution substations represents a complex task due to the difficult to determine the initial and final points where the disturbance occur. With the implementation of Smart Grids, the segmentation have been a crucial procedure to guarantee the better storage of data and improve the classification of disturbances. In this context, this paper provides a methodology based on Wavelet Transform and the determination of an adaptive threshold that allows the segmentation of voltage and/or current signals. So, we consider in this work short-duration voltage variations, impulsive and oscillatory transients, and harmonic distortions. In order to reach the above mentioned objectives, the disturbance signals were synthetically generated. Moreover, Gaussian noise was convolved on the signals with the intuit to obtain data closely to real power signals (voltage or current). Thus, these signals were used to validate the method proposed.