Affiliations: Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Correspondence:
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Corresponding author. Mohammad Mohammadi, Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran E-mail: m.mohammadi@shirazu.ac.ir.
Abstract: Differential protection of power transformers, as the fundamental protection, plays an important role in power system reliability and security. The main challenge in differential protection is discrimination between internal faults of power transformers and inrush current. Development of differential protection, especially discrimination between internal faults from other disturbances, have been a favorite subject in power system protection field over decades. Traditional methods proposed so far have several shortcomings: i) high computational burden, ii) sensitivity to noise, iii) being influenced by predefined threshold value/additional parameters/different models at varying ambient conditions, and iv) dependence on handcrafted or spectral analysis to extract features. Deep neural networks (DNN) is selected as the potential solution in this paper, which is able to capture the hierarchical features of a half-cycle of raw data. This paper proposes convolutional neural networks (CNN), in which batch normalization and scaled exponential linear unit (SELU) are merged to enhance differential protection performance. In order to generalize the CNN-based differential protection, several external factors, i.e. the compensation error of current transformer (CT) saturation, series compensated line, and superconducting fault current limiter (SFCL) are conducted to verify the reliability of the proposed method through different reliability metrics. The simulation and experimental results are assessed to show high reliability and the speed of the proposed method.
