Differential Protection of Power Transformers based on RSLVQ-Gradient Approach Considering SFCL

Conference paper (2021)

Authors

Shahabodin Afrasiabi Shiraz University
B. Behdani Shiraz University
Mousa Afrasiabi Shiraz University
Mohammad Mohammadi Shiraz University
Alia Asheralieva Southern University of Science and Technology
Mehdi Gheisari Southern University of Science and Technology, Islamic Azad University, Parand Branch
Affiliation
External organisation
More Info
expand_more

Published Date

28-06-2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Affiliation

External organisation

Abstract

One of the most challenging issues in protecting power transformers is to discriminate internal faults from inrush currents. This paper proposes a new approach for differential protection of power transformers based on the robust soft learning vector quantization (RSLVQ) method. Statistical features from the normalized differential current gradient are extracted in order to train the RSLVQ classifier. Furthermore, the performance of the proposed differential protection scheme is investigated in the presence of superconductor fault current limiter (SFCL), which can greatly affect the ability of differential protection schemes in correctly discriminating inrush from internal fault currents. The PSCAD/EMTDC software is utilized to generate sampled data in order to evaluate the performance of the proposed approach. The results obtained from the evaluation of the proposed method verified the promising performance of the RSLVQ-based differential protection scheme.