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EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

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)

  • Luis 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)

  • 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)

  • 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)

  • Homero Miranda-Vidales

    (Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, Av. Manuel Nava 8, Zona Universitaria, San Luis Potosi 78290, Mexico)

  • Arturo Garcia-Perez

    (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

In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:565-:d:74352
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    References listed on IDEAS

    as
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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. 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.
    3. Delong Cai & Kaicheng Li & Shunfan He & Yuanzheng Li & Yi Luo, 2018. "On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment," Energies, MDPI, vol. 11(2), pages 1-17, February.
    4. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
    5. Ferhat Ucar & Omer F. Alcin & Besir Dandil & Fikret Ata, 2018. "Power Quality Event Detection Using a Fast Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-14, January.
    6. Juan Carlos Bravo-Rodríguez & Francisco J. Torres & María D. Borrás, 2020. "Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-20, June.

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