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

David Granados-Lieberman,Rene Romero-Troncoso,Luis Morales-Hernandez,Roque Osornio-Rios,Martin Valtierra-Rodriguez

doi:10.3390/s130505507

David Granados-Lieberman, Rene Romero-Troncoso + Show 3 more

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https://doi.org/10.3390/s130505507

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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.

Highlights

Over the past few years, the power quality (PQ) has become an important issue in industrial and academic fields due to the growing number of disturbing loads in the industrial and public sectors; another important factor is the susceptibility that certain loads present to the presence of these disturbances
Different techniques have been used for analyzing Power quality disturbance (PQD), such as short-time Fourier transform (STFT), wavelet transform (WT), S-transform, Kalman filter, Gabor-Wigner, Hilbert transform, and Hilbert Huang transform [3,4,5,6,7,8,9,10,11,12,13]
A database with 200 signals is built for each one of the eight PQD, plus 200 for pure signals; these signals are generated in concordance with the equations and the parameters variation shown in Table 3, some of them have been used in [6] and [9],whereas the others are proposed in this research

Summary

Introduction

Over the past few years, the power quality (PQ) has become an important issue in industrial and academic fields due to the growing number of disturbing loads in the industrial and public sectors; another important factor is the susceptibility that certain loads present to the presence of these disturbances These anomalies are generally called power quality disturbances (PQD), which are deviations of voltage or current from the ideal sinusoidal waveform, such as sags, swells, interruptions, harmonics, flicker, notching, spikes, and oscillatory transients [1]. In real practice the WT capabilities are often significantly degraded in noisy environments [7] For this reason, other schemes based on S-transform [7,8,9], Kalman filter [10], and Gabor-Wigner transform [11] have been developed for detecting effectively

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