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.

Arising from solar storms, the emerging disturbances imposed on Earth's magnetic field, can drive the flow of quasi-DC geomagnetically induced currents (GICs) in power transmission systems. The ground connections of power transformers provide a closed path through which GICs flow and push their cores into half-cycle saturation. In addition, series capacitor units are often utilized to compensate HV transmission systems. The half-cycle saturation of power transformers due to GICs, on the one hand, and the proximity of such saturated transformers to capacitor compensation units on the other can lead to the inception of ferroresonance in series compensated power systems. To prevent catastrophic equipment failures due to ferroresonance during GICs, it is crucial to determine ferroresonance solutions of networks. This paper's principal contribution is to develop an approach to analyze ferroresonance in series capacitor compensated networks during GICs. This is attained by employing a simplified equivalent single-phase model on which to mount the analysis. The authenticity of this method is verified through an EMTP-RV simulation of a benchmark example power system.

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.