FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD

Misael Lopez-Ramirez, Eduardo Cabal-Yepez, Luis M. Ledesma-Carrillo, Homero Miranda-Vidales, Carlos Rodriguez-Donate and Rocio A. Lizarraga-Morales
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Misael Lopez-Ramirez: Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico
Eduardo Cabal-Yepez: Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico
Luis M. Ledesma-Carrillo: Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico
Homero Miranda-Vidales: Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, Av. Manuel Nava 8, Zona Universitaria, San Luis Potosi 78290, Mexico
Carlos Rodriguez-Donate: Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico
Rocio A. Lizarraga-Morales: Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico

Energies, 2018, vol. 11, issue 4, 1-15

Abstract: Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals.

Keywords: artificial neural networks; discrete wavelet transform; field programmable gate array; mathematical morphology; power quality disturbance; singular value decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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