| Autor: |
S. K. Bhasker, M. Tripathy, A. Agrawal, A. Mishra |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Journal of Operation and Automation in Power Engineering, Vol 11, Iss 2, Pp 123-129 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2322-4576 |
| DOI: |
10.22098/joape.2023.10004.1709 |
| Popis: |
An Indirect Symmetrical Phase Shift Transformer (ISPST) represents both electrically connected and magnetically coupled circuits, which makes it unique compared to a power transformer. Effective differentiation between transformer inrush current and internal fault current is necessary to avoid incorrect differential relay tripping. This research proposes a system that uses a Chebyshev Neural Network (ChNN) as a core classifier to distinguish such internal faults. For simulations, we used PSCAD/EMTDC software. Internal faults and inrush have been simulated in various ways using various ISPST parameters. A large, simulated dataset is used, and performance is recorded against different sized ISPSTs. We observed an overall accuracy greater than 99%. The ChNN classifier generated exceptionally favorable results even in case of noisy signal, CT saturation, and different ISPST parameters. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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