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Open AccessArticle

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

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HSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio, Qro. 76807, Mexico
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HSPdigital-CA Telematica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5 + 1.8, Palo Blanco, Salamanca, Gto. 36885, Mexico
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Author to whom correspondence should be addressed.
Sensors 2013, 13(5), 5507-5527; https://doi.org/10.3390/s130505507
Received: 12 March 2013 / Revised: 17 April 2013 / Accepted: 17 April 2013 / Published: 25 April 2013
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: Hilbert transform; power quality disturbances; power quality indices; instantaneous exponential time constant; FPGA; feed forward neural network; smart sensor Hilbert transform; power quality disturbances; power quality indices; instantaneous exponential time constant; FPGA; feed forward neural network; smart sensor
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MDPI and ACS Style

Granados-Lieberman, D.; Valtierra-Rodriguez, M.; Morales-Hernandez, L.A.; Romero-Troncoso, R.J.; Osornio-Rios, R.A. A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances. Sensors 2013, 13, 5507-5527.

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