CT Saturation Detection and Compensation: A Hybrid Physical Model- and Data-Driven Method
This addresses CT saturation issues for power system safety, representing an incremental improvement by integrating data-driven and physical models to avoid threshold adjustments and enhance generalization.
The paper tackles the problem of current transformer (CT) saturation, a major cause of relay protection malfunctions in power systems, by proposing a hybrid physical model- and data-driven method that detects and compensates for saturation to reproduce real waveforms, with simulation and experimental results verifying its effectiveness.
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.