Power quality assessment via wavelet transform analysis

TLDR

The paper proposes a new method to detect, localize, and assess the classification feasibility of various power quality disturbances. The method uses dyadic‑orthonormal wavelet transform with multiresolution decomposition to extract smoothed and detailed components, and analyzes squared wavelet coefficients to detect, localize, and characterize disturbances. The technique successfully detects and localizes disturbances and shows that squared wavelet coefficients can uniquely characterize different power quality disturbance types.

Abstract

In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.