A Comparative Analysis of Artificial Intelligence for Power Transformer Differential Protection

Conference paper (2021)

Authors

Shahabodin Afrasiabi Shiraz University

B. Behdani Shiraz University

Mousa Afrasiabi Shiraz University

Mohammad Mohammadi Shiraz University

Yang Liu Harbin Institute of Technology

Mehdi Gheisari Islamic Azad University, Parand Branch

Affiliation

External organisation

Artificial Intelligence (AI) Inrush current Internal fault CT Saturation Differential Protection Superconductor Fault Current Limiter (SFCL)

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Published Date

2021

Language

English

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Abstract

Power transformers being among the power system key components, are very important to protect. Various approaches have so far been proposed for this topic. Artificial intelligence (AI) has provided a concrete basis for numerous power transformer differential protection methodologies. However, the capabilities of AI-based methods have never been put into comparison. This paper provides a comparative analysis of various AI-based approaches for differential protection of power transformers. The performances of AI-based differential protection schemes are investigated in the presence of current transformer (CT) saturation condition, series capacitors compensation, and superconductor fault current limiter (SFCL), which possibly deteriorate the capability of differential protections to correctly tell inrush currents and internal faults apart. The PSCAD/EMTDC simulation tool is applied in producing the necessary test dataset for performance evaluation of the suggested novel strategy. The attained outcomes from the assessment of various methods have been analyzed to introduce the most superior AI-based differential protection scheme.