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 Islamic Azad University, Parand Branch, Southern University of Science and Technology

Affiliation

External organisation

Inrush current Internal fault Differential protection Normalized differential current gradient Robust Soft Learning Vector Quantizer (RSLVQ) Superconductor fault current limiter (SFCL)

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Published Date

28-06-2021

Language

English

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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.