Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Deep learning-based series AC arc detection algorithms

  • Park, Chang-Ju (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Dang, Hoang-Long (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Kwak, Sangshin (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Choi, Seungdeog (Department of Electrical and Computer Engineering, Mississippi State University)
  • Received : 2021.06.21
  • Accepted : 2021.08.04
  • Published : 2021.10.20
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Abstract

Various studies on arc detection methods are described. Series AC arc is detected based on the characteristics extracted from arc voltage, frequency, and time domain of the current. Methods of arc detection using artificial intelligence have been studied previously. In the present study, the performance of multiple methods is analyzed by comparing different input parameters and artificial neural networks. In addition to the input parameters presented in the literature, the performance is compared and analyzed using the following parameters: zero-crossing period, frequency average, instantaneous frequency, entropy, combination of fast Fourier transform (FFT) and maximum slip difference, and combination of FFT and frequency average. These parameters and different neural networks are studied in the bounded and unbounded case, and the performance is compared. For different combinations of neural networks and input parameters, another research question is to identify the input parameters to be used if the number of training data is limited. Moreover, this study investigates the change in detection rate depending on the number of training samples. As a result, the minimum dataset size required to obtain the final detection rate is identified.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A2C1013413) and the Korea Electric Power Corporation (Grant number: R21XA01-3).

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