A new fast discrete S-transform and decision tree for the classification and monitoring of power quality disturbance waveforms

In this paper, a new fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system. The proposed algorithm is based on the generalized Fourier algorithm that is used to obtain the time-localized spectral characteristics of the time-varying voltage and current signals belonging to PQ events. The fast ST algorithm is realized with different types of frequency scaling, band pass filtering, and interpolation techniques based on Heisenberg's uncertainty principle resulting in a reduced computation cost. In the conventional ST, the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Therefore, the time-varying PQ disturbance signal is down sampled at low frequencies and cropped at high frequencies resulting in the evaluation of a fewer samples. From the time-frequency matrix output, important features are extracted and used with a binary decision tree for an accurate classification of single and simultaneous PQ events. Further, a unified approach is presented to track the time-varying PQ disturbance waveforms like voltage sag, swell, harmonics, and oscillatory transients and produce estimation of their amplitudes and phase angles. Copyright (c) 2013 John Wiley & Sons, Ltd.