Abstract
Currently, the development of the power of the Internet of Things brings some challenges to all aspects of power system, one of which is the power supply problem of electric transmission line condition monitoring equipment. Energy-taking current transformer get the favor of the researchers at home and abroad because of its high practical value. Aiming at the existing problems of large starting current, easy saturation of iron core, and inability to adapt to wider electric transmission line current of energy-taking current transformer, many of them pay more attention to the research of post-stage circuit, but neglect the design and improvement of iron core structure. To solve the above problems, this paper mainly work as follows: on the basis of the initial permeability, saturation magnetic flux density and iron core loss and cost to determine the iron core material; Ansys software is used to analyze the iron core air gap shape design and simulation, through the iron core under different shape of air gap magnetic leak situation draw rectangular half opening effect optimal; Control variable method is used to analyze the air gap size simulation experiment, through the iron core of the saturated magnetic induction intensity and size of secondary side power output for the quantitative analysis of the air gap size. For the energy-taking current transformer’s iron core structure design and parameters of the actual selection provides a good engineering value.
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Funding
This research was funded by the Hebei College and Middle School Students Science and Technology Innovation Ability Cultivation Special Project (Grant No. 2021H011403), and the 2020 Education and Teaching Research Project of Polytechnic College of Hebei University of Science and Technology (Grant No. 2020Y03).
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Yu, P., Xu, Z., Zhao, X., Murray, E. (2022). Simulation Research on Iron Core Air Gap of Energy-Taking Current Transformer (CT) Based on Ansys. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_51
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