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Diverse conditions in power systems, such as massive use of nonlinear loads, continuous switching and operation of large electrical loads, and the integration of renewable energies, among others, have adversely affected the power quality (PQ) because they produce undesirable distortions in the waveforms of voltage and current. The conventional way to quantify the PQ is using the PQ indices (PQIs). Yet, the nonstationary properties of voltage and current signals degrade the PQIs estimation whenever classical techniques are used. In this paper, a methodology based on single-sideband modulation method and the Wavelet and Hilbert transforms for the estimation of instantaneous PQIs is proposed. It is shown that the proposal yields better tracking of transitory changes in the voltage/current signals than classical techniques such as the short-time Fourier transform. The PQIs used are the root-mean-square values, frequency, total harmonic distortion, active power, reactive power, apparent power, distortion power, power factor, and total power factor. PQIs performance is validated using synthetic and real signals. [ABSTRACT FROM PUBLISHER]

Accurate and fast characterization of voltage variations helps to evaluate their severity on equipment and activate protections. In this paper, a methodology for tracking and characterization of voltage variations, sample to sample, is presented. It consists of a Hilbert transform to estimate the voltage of the signal’s envelope, a fuzzy logic system to track down the type of voltage variation, and a rule-based method for the final identification and decision making according to IEEE Std 1159-2009. Unlike some techniques presented in the literature for tracking voltage variations such as the Kalman filter and adaptive linear network techniques, the proposed methodology requires neither a harmonic model nor an algorithm to adjust the model parameters, which in many cases increases the computational burden and time tracking. It is worth mentioning that the proposed classification stage does not need a training stage; therefore, its development is easier and its efficiency does not depend on a data training set. The performance of the proposed methodology is validated and tested using synthetic signals as well as real measurements of voltage variations. In addition, an implementation of our methodology into an field-programmable gate array based system is performed in an effort to offer a low-cost and portable system-on-a-chip solution for online and real-time monitoring of voltage variations. [ABSTRACT FROM PUBLISHER]