JPS_V3_N4_RP2 S. Deb S. Patra Journal on Power Systems Engineering 2322–0376 3 4 12 17 Voltage Sag, Swell, Power Quality, Wavelet Transform, Discrete Wavelet Transform, Multi Resolution Analysis, Detail and Approximate Wavelet Co-Efficient, Artificial Neural Networkt This paper presents an approach for measuring and classifying power quality disturbances using discrete wavelet transform and artificial neural network. The various power quality events are considered they are voltage sag, swell, harmonics, sag with harmonics, swell with harmonics and interruption. Due to the power quality disturbances, the signal is distorted. The energy of the distorted signal is first evaluated with the help of the Multi-Resolution Analysis (MRA) technique of Discrete Wavelet Transform (DWT) and the Parseval's theorem. Second, the energy deviation of the distorted signal with respect to pure sinusoidal signal is at different levels calculated. From these energy features and transient duration the artificial neural network classifies and identifies the disturbances. November 2015 - January 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=4801