A new fast discrete S-transform and decision tree for the classification and monitoring of power quality disturbance waveforms P. Ramesh Babu 1 , P. K. Dash 1 * ,† , S. K. Swain 1 and S. Sivanagaraju 2 1 Siksha ‘O’ Anusandhan University, Bhubaneswar, India 2 JNT University-K, Kakinada, Andhrapradesh, India SUMMARY 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 © 2013 John Wiley & Sons, Ltd. key words: power quality; discrete fast S-transform; disturbance pattern classification; decision tree; disturbance monitoring 1. INTRODUCTION Renewable sources of energy create new power problems, such as voltage variations, flicker, and waveform distortion. Poor power quality (PQ) is attributed due to the various power system disturbances like voltage sag, swell, impulsive, and oscillatory transients, multiple notches, momentary interruption, harmonics, etc. In addition, certain power electronically controlled electric drives, voltage source inverters used in renewable energy systems, saturated transformers, use of nonlinear loads, etc. can cause severe PQ problems. Thus, to improve the quality of electrical power supplied to the customers, it is imperative to continuously monitor the power network disturbance waveforms through the use of hardware-based PQ monitoring and classification systems. The aim of an efficient power network is to transport electrical energy to different customers with acceptable voltage and frequency and free from harmonic distortions. Therefore, detection and classi- fication of voltage/current variations and other steady-state and transient PQ problems are relevant to determine their sources of origin and devise suitable methods to manage them. Also, due to the increased importance of PQ problems in the distribution networks, it has become important to estimate the various PQ indices and take appropriate steps to mitigate them. Also, the PQ disturbance phasors need to be evaluated in the recent spurt of activities in the area of renewable energy systems like the *Correspondence to: P. K. Dash, Siksha ‘O’ Anusandhan University, Bhubaneswar, India. † E-mail: pkdash.india@gmail.com Copyright © 2013 John Wiley & Sons, Ltd. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS Int. Trans. Electr. Energ. Syst. (2013) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/etep.1776