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Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth

Author

Listed:
  • Jerzy Baranowski

    (Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Katarzyna Grobler-Dębska

    (Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Edyta Kucharska

    (Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

Abstract

Diagnostics of power and energy systems is obviously an important matter. In this paper we present a contribution of using new methodology for the purpose of signal type recognition (for example, faulty/healthy or different types of faults). Our approach uses Bayesian functional data analysis with data depths distributions to detect differing signals. We present our approach for discrimination of pole-to-pole and pole-to-ground short circuits in VSC DC cables. We provide a detailed case study with Monte Carlo analysis. Our results show potential for applications in diagnostics under uncertainty.

Suggested Citation

  • Jerzy Baranowski & Katarzyna Grobler-Dębska & Edyta Kucharska, 2021. "Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth," Energies, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5893-:d:637628
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Nagy, Stanislav & Ferraty, Frédéric, 2019. "Data depth for measurable noisy random functions," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 95-114.
    3. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
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    Cited by:

    1. Jakub Poręba & Jerzy Baranowski, 2022. "Functional Logistic Regression for Motor Fault Classification Using Acoustic Data in Frequency Domain," Energies, MDPI, vol. 15(15), pages 1-12, July.

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