Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers
نویسندگان
چکیده
Differential protection, as the key protection element in power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential needs an additional block distinguish between internal faults and The system should, first, be able perform based on raw data, second, learn fully temporal features sudden changes signals, and, third, impose no assumption noise. To address these challenges, fast RNN, namely gated recurrent neural network (FGRNN). By removing reset gate unit (GRU), proposed is capable of learning abrupt addition significantly reducing computational time. Furthermore, loss function information theory concept formulated this article enhance ability well robustness against non-Gaussian/Gaussian noises. A generalized form mutual also adopted noise model-free function, then incorporated designed deep network. Simulated experimental examinations engaging various factors, comparison FGRNN, GRU, seven firmly established methods indicates faster more reliable performance algorithm.
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a neuro-fuzzy technique for discrimination between internal faults and magnetizing inrush currents in transformers
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ژورنال
عنوان ژورنال: IEEE Transactions on Industrial Electronics
سال: 2022
ISSN: ['1557-9948', '0278-0046']
DOI: https://doi.org/10.1109/tie.2021.3109535