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CT Saturation Detection and Compensation: A Hybrid Physical Model- and Data-Driven Method.
- Source :
- IEEE Transactions on Power Delivery; Oct2022, Vol. 37 Issue 5, p3928-3938, 11p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858977
- Volume :
- 37
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Delivery
- Publication Type :
- Academic Journal
- Accession number :
- 160691586
- Full Text :
- https://doi.org/10.1109/TPWRD.2022.3141550