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Power Quality Transient Detection and Characterization Using Deep Learning Techniques

Author

Listed:
  • Nuno M. Rodrigues

    (Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
    These authors contributed equally to this work.)

  • Fernando M. Janeiro

    (Instituto de Telecomunicações, Universidade de Évora, 7000-671 Évora, Portugal
    These authors contributed equally to this work.)

  • Pedro M. Ramos

    (Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
    These authors contributed equally to this work.)

Abstract

Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.

Suggested Citation

  • Nuno M. Rodrigues & Fernando M. Janeiro & Pedro M. Ramos, 2023. "Power Quality Transient Detection and Characterization Using Deep Learning Techniques," Energies, MDPI, vol. 16(4), pages 1-11, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1915-:d:1069004
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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.
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    Cited by:

    1. Tatjana Baraškova & Karolina Kudelina & Veroonika Shirokova, 2024. "New Opportunities in Real-Time Diagnostics of Induction Machines," Energies, MDPI, vol. 17(13), pages 1-16, July.
    2. Jingyi Zhang & Tongtian Sheng & Pan Gu & Miao Yu & Honghao Wu & Jianqun Sun & Jinming Bao, 2024. "Comprehensive Power Quality Assessment Based on a Data-Driven Determinant-Valued Extension Hierarchical Analysis Approach," Energies, MDPI, vol. 17(13), pages 1-14, June.

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