A1 Journal article (refereed), original research (Journal article, original research)

Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers

Open Access publication

Publication Details

Authors: Afrasiabi Shahabodin, Afrasiabi Mousa, Parang Benyamin, Mohammadi Mohammad, Samet Haidar, Dragicevic Tomislav

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication year: 2021

Language: English

Related journal or series: IEEE Transactions on Industrial Electronics

Journal name in source: IEEE Transactions on Industrial Electronics

ISSN: 0278-0046

eISSN: 1557-9948

JUFO level of this publication: 3

Digital Object Identifier (DOI): http://dx.doi.org/10.1109/TIE.2021.3109535

Permanent website address: https://ieeexplore.ieee.org/document/9531375/

Social media address: https://www.researchgate.net/publication/354456555_Fast_GRNN-Based_Method_for_Distinguishing_Inrush_Currents_in_Power_Transformers


Open Access: Open Access publication

Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2021120158155


Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs an additional block to distinguish between internal faults and external transient disturbances. The protection system should i) be able to perform based on raw data, ii) be able to learn fully temporal features and sudden changes in the transient signals, and iii) impose no assumption on noise. To address these challenges, a fast recurrent neural network, namely fast gated recurrent neural network (FGRNN). By removing the reset gate in the gated recurrent unit (GRU), the proposed network is capable of learning abrupt changes in addition to significantly reducing the computational time. Furthermore, a loss function based on an information theory concept is formulated in this paper to enhance the learning ability as well as robustness against non-Gaussian/Gaussian noises. A generalized form of mutual information is also adopted to form a noise model-free loss function, then incorporated with the designed deep network. Simulated and experimental examinations engaging various external factors, in addition to comparison between the proposed fast GRNN, GRU, and seven firmly-established methods indicates the faster and more reliable performance of the proposed algorithm.

KeywordsDeep learning, Differential protection, Fast gated recurrent neural network (FGRNN), Fault currents, Feature extraction, Inrush current, Internal fault, Logic gates, Real-time systems, Transient analysis

Last updated on 2021-09-12 at 09:13