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Research into an Online Calibration System for the Errors of Voltage Transformers Based on Open–Closed Capacitor

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  • Zhenhua Li

    (College of Electrical Engineering & New Energy, China Three Gorges University College, Yichang 443002, China
    Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China)

  • Qiuhui Li

    (College of Electrical Engineering & New Energy, China Three Gorges University College, Yichang 443002, China)

  • Zhengtian Wu

    (School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China)

  • Zhenxing Li

    (College of Electrical Engineering & New Energy, China Three Gorges University College, Yichang 443002, China)

Abstract

Periodical calibration is necessary to ensure the accuracy and reliability of voltage transformers. The traditional calibration method requires the power to be off, and the calibration period for this method is too long, meaning that problems with the transformer cannot be found in time. In this paper, a voltage transformer error online calibration system based on open–closed capacitors is proposed. Two open–closed capacitors and other auxiliary devices are utilized to construct the standard voltage sensor. The outputs of the open–closed capacitors are compared with each other to realize accurate self-checking. The average value of the output is used as the final output, which can improve the system’s accuracy and reliability. An improved algorithm based on a hybrid convolution window is proposed to extract the fundamental and harmonic signals. Test results show that the variation of the ratio error is less than 0.037%, and the variation of the angle error is less than 0.45’.

Suggested Citation

  • Zhenhua Li & Qiuhui Li & Zhengtian Wu & Zhenxing Li, 2018. "Research into an Online Calibration System for the Errors of Voltage Transformers Based on Open–Closed Capacitor," Energies, MDPI, vol. 11(6), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1455-:d:150711
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    Cited by:

    1. Jun He & Zhihao Zhou & Chao Tong & Fan Li & Fangxi Rao & Qiu Xu, 2023. "Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning," Energies, MDPI, vol. 16(12), pages 1-15, June.

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