Power quality disturbance magnitude characterization using wavelet transformation analysis part 1-theory

Abstract

In this paper, a few approaches to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances are presented. The approaches are based on wavelet transform analysis, particularly the Paul, Gaussian, and Daubechies wavelet transform. The key idea underlying the approaches are to decompose a given disturbance signal into other signals which represents transforming a one-dimensional time series into two-dimensional time-frequency space. The decomposition is performed using the Paul, Gaussian, and Daubechies wavelet transform techniques. The techniques to detect and localize disturbances with actual power line disturbances are proposed, and then demonstrated and tested. In order to enhance the detection outcomes the squared wavelet transform coefficients of the analyzed power line signal are utilized. Based on the results of the detection and localization, an initial investigation of the ability to uniquely characterize various types of power quality disturbances is carried out. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.