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

Abstract

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 a comparison between the proposed fast GRNN, GRU, and seven firmly-established methods indicates the faster and more reliable performance of the proposed algorithm.