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An End-to-End Deep Learning Method for Voltage Sag Classification

Author

Listed:
  • Radovan Turović

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Dinu Dragan

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Gorana Gojić

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Veljko B. Petrović

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Dušan B. Gajić

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Aleksandar M. Stanisavljević

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

  • Vladimir A. Katić

    (Faculty of Technical Sciences, University of Novi Sad, 21460 Novi Sad, Serbia)

Abstract

Power quality disturbances (PQD) have a negative impact on power quality-sensitive equipment, often resulting in great financial losses. To prevent these losses, besides detecting a PQD on time, it is important to classify it, so that appropriate recovery procedures are employed. The majority of research employs machine learning model PQD classifiers on manually extracted features from simulated or real-world signals. This paper presents an end-to-end approach that circumvents the manual feature extraction and uses signals generated from mathematical voltage sag type formulas. We developed a configurable voltage sag generator that was used to form training and validation datasets. Based on the synthetic three-phase voltage signals, we trained several end-to-end LSTM classifiers that classify voltage sags according to ABC classification. The best-performing model achieved an accuracy of over 90% in the real-world dataset.

Suggested Citation

  • Radovan Turović & Dinu Dragan & Gorana Gojić & Veljko B. Petrović & Dušan B. Gajić & Aleksandar M. Stanisavljević & Vladimir A. Katić, 2022. "An End-to-End Deep Learning Method for Voltage Sag Classification," Energies, MDPI, vol. 15(8), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2898-:d:794490
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    References listed on IDEAS

    as
    1. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
    2. 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.
    3. Haoyuan Sha & Fei Mei & Chenyu Zhang & Yi Pan & Jianyong Zheng, 2019. "Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine," Energies, MDPI, vol. 12(6), pages 1-15, March.
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    Cited by:

    1. Yunus Yalman & Tayfun Uyanık & Adnan Tan & Kamil Çağatay Bayındır & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data," Mathematics, MDPI, vol. 10(19), pages 1-13, September.

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