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.
@article{arxiv.2604.05334,
title = {CT Saturation Detection and Compensation: A Hybrid Physical Model- and Data-Driven Method},
author = {Songhao Yang and Yubo Zhang and Zhiguo Hao and Zexuan Lin and Baohui Zhang},
journal= {arXiv preprint arXiv:2604.05334},
year = {2026}
}