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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Misael Lopez-Ramirez & Eduardo Cabal-Yepez & Luis M. Ledesma-Carrillo & Homero Miranda-Vidales & Carlos Rodriguez-Donate & Rocio A. Lizarraga-Morales, 2018. "FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD," Energies, MDPI, vol. 11(4), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:769-:d:138456
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    References listed on IDEAS

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    1. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
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    4. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.
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    Cited by:

    1. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    3. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.

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