A sparse representation based approach for recognition of power system transients

Abstract

The present work shows how a new genre of problem solution techniques called compressive sensing (CI) can be utilized for solving power quality problems. Specifically speaking, this work undertakes the power system transient recognition problem and solves it using sparse representation based classification (SRC) technique, a popular, recently emerging technique in CI. The method employs SRC based classification in combination with a feature extraction procedure, carried out using dual tree complex wavelet transform (DTCWT). The proposed method uses statistical characteristics to extract features from the DTCWT coefficients obtained from each signal and then these extracted features are used as input arrays for the SRC based classifier. The PQ disturbance events considered in this work include four different transient conditions, namely, transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. The proposed algorithm could achieve perfect classification accuracy of the order of 100% and could comfortably outperform several similar contemporary methods known for identical power system transient classification problems.