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A Non-Invasive Circuit Breaker Arc Duration Measurement Method with Improved Robustness Based on Vibration–Sound Fusion and Convolutional Neural Network

Author

Listed:
  • Ning Guo

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

  • Kevin Whitmore

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

  • Morris Cohen

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

  • Raheem Beyah

    (College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

  • Lukas Graber

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA)

Abstract

Previous studies have shown that the contact wear estimation of circuit breakers can be based on the accumulative arc duration. However, one problem that remains unresolved is how to reliably measure the arc duration. Existing methods encounter difficulties in implementation and suffer from limited accuracy owing to the impact of the substation environment. To overcome these issues, this article presents a novel, non-invasive method for measuring arc duration that combines vibration–sound fusion and convolutional neural network. The proposed method demonstrates excellent performance, achieving errors below 0.1 ms under expected noise conditions and less than 1 ms in the presence of various forms of noise, transient interference, and even sensor failure. Its advantages include its ability to accurately measure arc duration and its robustness against noise and interference with unknown patterns and varying intensity as well as sensor failure. These features make it highly suitable for practical deployment in substation environments.

Suggested Citation

  • Ning Guo & Kevin Whitmore & Morris Cohen & Raheem Beyah & Lukas Graber, 2023. "A Non-Invasive Circuit Breaker Arc Duration Measurement Method with Improved Robustness Based on Vibration–Sound Fusion and Convolutional Neural Network," Energies, MDPI, vol. 16(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6551-:d:1238055
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