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http://dx.doi.org/10.5909/JBE.2019.24.5.791

Electrical Arc Detection using Artificial Neural Network  

Lee, Sangik (Electrical Safety Research Institute & Korea Electrical Safety Corp.)
Kang, Seokwoo (Electrical Safety Research Institute & Korea Electrical Safety Corp.)
Kim, Taewon (Electrical Safety Research Institute & Korea Electrical Safety Corp.)
Lee, Seungsoo (Dept. of Computer & Communications Eng., Kangwon National University)
Kim, Manbae (Dept. of Computer & Communications Eng., Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.24, no.5, 2019 , pp. 791-801 More about this Journal
Abstract
The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. Therefore, there is a need to develop a method that could increase the feature dimension, thereby improving the detection performance. In this paper, we use variational mode decomposition (VMD) to obtain multiple decomposed signals and then extract statistical features from them. The features from VMD outperform those from no-VMD in terms of detection performance. Further, artificial neural network is employed as an arc classifier. Experiments validated that the use of VMD improves the classification accuracy by up to 4 percent, based on 14,000 training data.
Keywords
arc detection; deep neural network; variational mode decomposition; statistical features;
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