Time-Frequency Analysis of Non-Stationary Waveforms in Power-Quality via Synchrosqueezing Transform

An accurate time-frequency representation (TFR) can provide useful information in non stationary data analysis and processing. The traditional methods like short-time Fourier transform (STFT) and wavelet transform (WT) based TFR approaches are leads to a tradeoff between time and frequency resolution. A recently proposed synchrosqueezing transform (SST), which is an extension of the WT, has been successfully used as a potential tool for time-frequency analysis of signals with time varying characteristics. This paper introduces a novel approach for the visual localization and detection of various non-stationary power quality disturbances present in a power network using SST. In this study we considered different types of power-quality (PQ) disturbances as input data. The extensive simulation results show that the SST based approach is very much effective in characterization of PQ events compared to traditional continues wavelet transform (CWT).