4.7 Article

Research on Residual Flux Density Measurement for Single-Phase Transformer Core Based on Energy Changes

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3117087

关键词

Transformer cores; Iron; Current measurement; Magnetic domains; Circuit faults; Current transformers; Magnetic circuits; Empirical formula; energy changes; finite element method (FEM); power transformer; residual flux density

资金

  1. National Natural Science Foundation of China [51877065]
  2. Science and Technology Research Project of Higher Education Institutions in Hebei Province of China [QN2016200]

向作者/读者索取更多资源

This article proposes a residual flux density measurement method based on energy changes for a single-phase transformer core. The direction of residual flux density is determined by comparing energy changes, and the magnitude is calculated through an empirical formula. The method is validated to have higher accuracy than existing methods with a relative error within 5%.
When the power transformer is reconnected to the power grid, and if there is a large residual flux in the iron core, an inrush current may result. To suppress inrush current, the detailed information of the residual flux needs to be known. In this article, a residual flux density measurement method for a single-phase transformer core based on energy changes is proposed. First, when positive and negative dc voltages (referred to as the direction along or opposite to the initial residual flux density) are applied, the positive and negative energy changes of the power source can be obtained by measured response currents. Then, the direction of the residual flux density is determined by comparing the gained energy changes. The magnitude of residual flux density can be calculated by the empirical formula between residual flux density and positive energy. The square iron core is investigated in this work to establish the corresponding empirical formula by finite element method. Finally, the accuracy of the empirical formula is verified on the experimental platform. The results show that the relative error of the proposed method is within 5%, which has higher accuracy than existing methods.

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