Full Text

Turn on search term navigation

© 2022 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

An optical DC current transformer anomaly handling mechanism is proposed to address the problem that the conventional DC current transformer anomaly handling mechanism cannot compensate for the defect of capacitor anomaly blocking. First, the implementation principle, modulation loop, demodulation method and its anomaly warning mechanism of the sine-wave modulated all-fibre-optic current transformer (FOCT) are investigated, and the effects of light source intensity and modulation voltage on current decoding are explained. The modulation loop is then simulated and modelled and a FOCT anomaly handling mechanism is proposed based on the Bessel function with real-time dynamic current compensation for small changes in modulation depth. Finally, an integrated dynamic test system for DC current transformers and DC protection is designed, and the actual system operation and fault model is established using the RTDS simulation system. The experiments demonstrate that the proposed FOCT anomaly handling and improvement measures can effectively improve the transient performance of FOCT, and at the same time provide a complete set of testing means for the engineering application and later upgrade and replacement of FOCT.

Details

Title
Abnormal handling mechanism and improvement measures of optical DC current transformer in smart grid environment
Author
Zhou, Shihao; Tang, Hansong; Pan, Benren; Zhang, Wei
Publication year
2022
Publication date
Nov 1, 2022
Publisher
PeerJ, Inc.
e-ISSN
23765992
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2730879487
Copyright
© 2022 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.