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A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances

Author

Listed:
  • Azam Bagheri

    (AI & Future Technologies, Industrial and Digital Solutions, ÅF Pöyry AB (Afry), 411 19 Gothenburg, Sweden)

  • Roger Alves de Oliveira

    (Electric Power Engineering, Luleå University of Technology, 931 87 Skellefteå, Sweden)

  • Math H. J. Bollen

    (Electric Power Engineering, Luleå University of Technology, 931 87 Skellefteå, Sweden)

  • Irene Y. H. Gu

    (Department Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden)

Abstract

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.

Suggested Citation

  • Azam Bagheri & Roger Alves de Oliveira & Math H. J. Bollen & Irene Y. H. Gu, 2022. "A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances," Energies, MDPI, vol. 15(4), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1283-:d:746039
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
    2. Aleksandr Skamyin & Yaroslav Shklyarskiy & Ilya Gurevich, 2024. "Influence of Background Voltage Distortion on Operation of Passive Harmonic Compensation Devices," Energies, MDPI, vol. 17(6), pages 1-16, March.

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