A quantitative comparison of wavelet based feature vectors for classification of power quality disturbances

Abstract

This paper presents a comparison between different wavelet feature vectors for power quality disturbance classification problems. Three different wavelet algorithms are simulated and applied on nine classes of power quality disturbances. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results.