Power quality disturbance magnitude characterization using wavelet transformation analysis part 2 – application

Power quality disturbances (PQD) are normally monitored by dedicated power quality devices. The devices capture disturbances waveform in real-time. Magnitude is accepted as a significant index for detection, general classification and later assessment analysis. To choose suitable way of magnitude characterization is a fundamental work of PQD measuring and monitoring. This study presents three different ways, RMS voltage, peak voltage and fundamental voltage component, to determine magnitude. The algorithms of the three approaches implemented are wavelet transformation (WT) based. In this paper, several approaches to detect, localize, and investigate the feasibility of classifying various types of PQD are presented. The approaches are based on wavelet transform analysis, particularly the Paul, Gaussian and Daubechies wavelet transform. The key idea underlying the approaches is to decompose a given disturbance signal into time-frequency phase, which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using signal decomposition techniques. The proposed technique to detect and localize disturbances with actual power line disturbances is demonstrated and tested. 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 PQD is carried on. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each PQD