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Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth.
- Source :
-
Energies (19961073) . Sep2021, Vol. 14 Issue 18, p5893-5893. 1p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 14
- Issue :
- 18
- Database :
- Academic Search Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 152657059
- Full Text :
- https://doi.org/10.3390/en14185893