Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth

Jerzy Baranowski, Katarzyna Grobler-Dębska and Edyta Kucharska
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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

Energies, 2021, vol. 14, issue 18, 1-17

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.

Keywords: cable diagnostics; Bayesian statistics; functional data analysis; uncertainty; Hamiltonian Monte Carlo; data depth (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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