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Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Author

Listed:
  • Enrique Reyes-Archundia

    (Tecnológico Nacional de México, Instituto Tecnológico de Morelia, Morelia 58120, Mexico)

  • Wuqiang Yang

    (Deptartment of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK)

  • Jose A. Gutiérrez Gnecchi

    (Tecnológico Nacional de México, Instituto Tecnológico de Morelia, Morelia 58120, Mexico)

  • Javier Rodríguez-Herrejón

    (Tecnológico Nacional de México, Instituto Tecnológico de Morelia, Morelia 58120, Mexico)

  • Juan C. Olivares-Rojas

    (Tecnológico Nacional de México, Instituto Tecnológico de Morelia, Morelia 58120, Mexico)

  • Aldo V. Rico-Medina

    (Tecnológico Nacional de México, Instituto Tecnológico de Morelia, Morelia 58120, Mexico)

Abstract

Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

Suggested Citation

  • Enrique Reyes-Archundia & Wuqiang Yang & Jose A. Gutiérrez Gnecchi & Javier Rodríguez-Herrejón & Juan C. Olivares-Rojas & Aldo V. Rico-Medina, 2024. "Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances," Energies, MDPI, vol. 17(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2281-:d:1391133
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    References listed on IDEAS

    as
    1. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    2. Talada Appala Naidu & Hamad Mohamed Ali Ahmed Albeshr & Ammar Al-Sabounchi & Sajan K. Sadanandan & Tareg Ghaoud, 2023. "A Study on Various Conditions Impacting the Harmonics at Point of Common Coupling in On-Grid Solar Photovoltaic Systems," Energies, MDPI, vol. 16(17), pages 1-31, September.
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