S-Transform with a Compact Support Kernel and Classification Models Based Power Quality Recognition

In this paper, a novel method for power quality (PQ) events recognition is presented. Nine types of PQ events consisting of single and multi-stage disturbances are considered for study. For this task, features observed in the time frequency (t, f) plane have been used. Synthetic PQ events are generated using mathematical models. These signals are then projected in the time-frequency plane via the ST with a Compact Support Kernel (ST-CSK) providing the time-frequency resolution, energy concentration and robustness to noise. In this plane, PQ events are localized and characterized. The extracted features are then classified using several technics. The achieved results show that an overall accuracy of 100% has been obtained with Support Vector Machines and Random Forest classifiers even with signals embedded in high Additive White Gaussian Noise level (SNR=5dB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SNR}=5\,\hbox {dB}$$\end{document}). In the same conditions, XGboost classifier accurately detects 99.72% of PQ events.