CT Saturation Detection and Compensation: A Hybrid Physical Model- and Data-Driven Method

TL;DR

This paper introduces a hybrid approach combining physical modeling and data-driven techniques to detect and compensate for current transformer saturation, improving relay protection reliability.

Contribution

It develops a novel method integrating FCN-based detection with physical short-circuit models, enhancing accuracy and generalization over existing approaches.

Findings

The method effectively detects CT saturation in simulations and experiments.

It accurately compensates for saturation, restoring true waveform characteristics.

The approach reduces the need for universal threshold tuning and improves interpretability.

Abstract

Current transformer (CT) saturation is one of the dominant causes of relay protection devices' malfunctions, which pose a threat to the safe operation of the power system. To address this problem, we propose a hybrid physical model- and data-driven method. The method firstly detects the CT saturation and then compensates it to reproduce the real waveform. Considering the multi-factor and strong nonlinearity of CT saturation, a data-driven model, namely the Fully Convolutional Network (FCN), is built to detect the operation status of CT. As for the compensation, a physical model of short-circuit current is used for its conciseness and universality. Through tactfully integrating the data model and the physical model, the proposed method is endowed with two major merits: the arduous adjustment of universal thresholds and parameters in existing methods is avoided, and the deficiency in generalization and interpretability of the data-driven method is assuaged. Simulation and experimental results verify the effectiveness of the proposed method. Furthermore, its application potential to future protection is explored.