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

被引：0
作者：
Afrasiabi, Shahabodin [1 ]
Behdani, Behzad [1 ]
Afrasiabi, Mousa [1 ]
Mohammadi, Mohammad [1 ]
Asheralieva, Alia [2 ]
Gheisari, Mehdi [2 ,3 ]
机构：
[1] Shiraz Univ, Sch Elect & Comp Engn, Dept Power & Control Engn, Shiraz, Iran
[2] Southern Univ Technol & Sci, Dept Comp Sci, Shenzhen, Peoples R China
[3] Islamic Azad Univ, Parand Branch, Young Researchers & Elite Club, Parand, Iran
来源：
2021 IEEE MADRID POWERTECH | 2021年
基金：
中国国家自然科学基金;
关键词：
Differential protection; inrush current; internal fault; Normalized differential current gradient; Robust Soft Learning Vector Quantizer (RSLVQ); Superconductor fault current limiter (SFCL); INRUSH CURRENT; INTERNAL FAULTS; MAGNETIZING INRUSH; DISCRIMINATION; CURRENTS; CLASSIFICATION;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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.
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页数：6
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