Characterization of distribution power quality events with Fourier and wavelet transforms

TLDR

These features are derived from well documented theories, power engineers' heuristics gained through long years of experience, and power quality data collected in recent years. The paper aims to present unique features that characterize power quality events and methods to extract them from recorded voltage and/or current waveforms using Fourier and wavelet transforms. The authors illustrate the features—peak amplitudes, RMS, frequency, and wavelet coefficient statistics—and apply them to three common distribution‑level events: converter operation, transformer energization, and capacitor energization (normal, back‑to‑back, and re‑strike on opening). These examples provide the basis for further characterization of other power quality events.

Abstract

It is the objective of this paper to present unique features that characterize power quality events and methodologies to extract them from recorded voltage and/or current waveforms using Fourier and wavelet transforms. Examples of unique features include peak amplitudes, RMS, frequency, and statistics of wavelet transform coefficients. These features are derived from well documented theories, power engineers' heuristics gained through long years of experience, and power quality data collected in recent years. Converter operation, transformer energization, and capacitor energization (which includes normal, back-to-back, and re-strike on opening energization), representing three common power quality events at the distribution level, are presented. These examples provide the basis for further characterization of other power quality events.