TY - GEN
T1 - Differential Protection of Power Transformers based on RSLVQ-Gradient Approach Considering SFCL
AU - Afrasiabi, Shahabodin
AU - Behdani, Behzad
AU - Afrasiabi, Mousa
AU - Mohammadi, Mohammad
AU - Asheralieva, Alia
AU - Gheisari, Mehdi
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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.
AB - 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.
KW - Differential protection
KW - inrush current
KW - internal fault
KW - Normalized differential current gradient
KW - Robust Soft Learning Vector Quantizer (RSLVQ)
KW - Superconductor fault current limiter (SFCL)
UR - http://www.scopus.com/inward/record.url?scp=85112366320&partnerID=8YFLogxK
U2 - 10.1109/PowerTech46648.2021.9494873
DO - 10.1109/PowerTech46648.2021.9494873
M3 - Conference contribution
AN - SCOPUS:85112366320
T3 - 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
BT - 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
PB - IEEE
T2 - 2021 IEEE Madrid PowerTech, PowerTech 2021
Y2 - 28 June 2021 through 2 July 2021
ER -
