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Series arc fault diagnosis using generalized S-Transform and power spectral density
  • Penghe Zhang,
  • Yiwei Qin
Penghe Zhang
China Electric Power Research Institute
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Yiwei Qin
China Electric Power Research Institute

Corresponding Author:qinyiwei0928@163.com

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Abstract

It is difficult to identify the arc fault effectively when the loads in the user-side are more complicated, blocking the development of low-voltage monitoring and pre-warning inspection. In this paper, series arc fault signals are acquired according to IEC 62606. The main time-frequency features can be strengthened more effectively by the generalized S-transform with bi-Gaussian window, meanwhile the power spectrum density (PSD) determination allows for the detection of imperceptible high-frequency harmonics energy reflections, increasing the rate of arc fault diagnosis and suitable for the arc fault monitoring of nonlinear loads. The final samples are trained and classified by two-dimensional Convolutional Neural Network (CNN) and the overall accuracy of identification is 98.13%, of which involves various domestic loads, providing a reference for the follow-up arc fault monitoring and inspection research.
24 Mar 2024Submitted to IET Generation, Transmission & Distribution
25 Apr 2024Review(s) Completed, Editorial Evaluation Pending
25 Apr 2024Editorial Decision: Revise Major
15 May 20241st Revision Received
16 May 2024Submission Checks Completed
16 May 2024Assigned to Editor
16 May 2024Reviewer(s) Assigned