Application of artificial neural networks for saturation correction in current and voltage transformers
The article investigates the application of artificial neural networks for saturation correction in current and voltage transformers. Under saturation conditions, these transformers can distort signals, leading to the incorrect operation of measuring and protection devices. The use of artificial n...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2025-06-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2025/%E2%84%962(194)/89-95%20%D0%A2%D0%B5%D0%BC%D0%BD%D0%B8%D0%BA%D0%BE%D0%B2%20%D0%95.%20%D0%90.,%20%D0%9D%D0%B8%D0%BA%D0%B8%D1%82%D0%B8%D0%BD%20%D0%9A.%20%D0%98..pdf |
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| Summary: | The article investigates the application of artificial neural networks for saturation correction in current
and voltage transformers. Under saturation conditions, these transformers can distort signals, leading to the
incorrect operation of measuring and protection devices. The use of artificial neural networks allows increasing
accuracy in signal processing, thereby improving the reliability and safety of electric power systems. The
paper describes methods for training neural networks using historical data, modeling transformer operation
under various conditions, and developing algorithms for correcting distortions caused by saturation. |
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| ISSN: | 1813-8225 2541-7541 |