Abstract
During fault or transient conditions, the current in the secondary winding of conventional current transformers (CCT) undergoes significant distortion due to residual magnetization of the core caused by a primary current increase. This phenomenon can lead to non-selective or false tripping of relay protection and automation devices, meanwhile the current in the measuring circuits can tend to zero. Nowadays, several methods to compensate the effect of current transformer saturation on secondary winding exist in digital substations. To understand the advantages and disadvantages of various methods, this paper provides a comparative analysis and numerical evaluation of several CCT saturation compensation methods. In this paper, a model of a saturated CCT operating under a three-phase short circuit was studied. As a result, it was found that none of the existing methods can be applied to real-time compensation of CCT saturation in a real power system. In addition to examining and comparing the existing methods, this article presents relatively new methods for secondary current waveform reconstruction based on machine learning. To study its effectiveness, 44.000 cases of CCT saturation were simulated. The most effective methods demonstrated the ability to fully restore secondary current in saturated mode with an average error of approximately 2%. According to the results, further study of this issue regarding the possibility of applying this method in real power systems are needed.
Keywords
saturation of measuring current transformers, restoration of the secondary current waveform, differentiation, equivalent circuit, Gills-Atherton model, least squares method, neural network, machine learning
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