A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances

Abstract

Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.

Power disturbance identification based on transient behaviors using morphological max-lifting scheme and nonlinear principal component analysis

“…In the past few years, power disturbances have become an important issue in industrial and academic fields due to the serious power and economic loss caused by them [1]. To limit the negative effects of power disturbances on power systems, it is necessary to identify these power disturbances quickly and accurately.…”

Alternative definitions of energy for power meters in non-sinusoidal systems

“…The detection and classification of voltage disturbances can be better accomplished by the use of smart sensors [23], or by dip detectors such as generalized likelihood ratio test (GLRT) [24], Kalman filters, peak voltage, missing voltage, short-time Fourier transform (STFT), wavelet transform (WT), S-transform, Gabor-Wigner transform and neuronal networks [25,26].…”

FPGA-based instantaneous estimation of unbalance/symmetrical components through the Hilbert transform

“…The HT is a mathematical tool used for tracking the voltage envelope in power quality [16][17][18][19][20], which is defined for real signals as:…”