Browse > Article
http://dx.doi.org/10.5909/JBE.2020.25.4.569

Electrical Arc Detection using Convolutional 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.)
Kim, Manbae (Dept. of Computer & Communications Eng., Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.25, no.4, 2020 , pp. 569-575 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, additional steps such as transformation and feature extraction are required. On the contrary, deep learning models directly use the raw data without any feature extraction processes. Therefore, the usage of time-domain data is preferred, but the performance is not satisfactory. To solve this problem, subsequent 1-D signals are transformed into 2-D data that can feed into a convolutional neural network (CNN). Experiments validated that CNN model outperforms deep neural network (DNN) by the classification accuracy of 8.6%. In addition, data augmentation is utilized, resulting in the accuracy improvement by 14%.
Keywords
arc detection; convolutinoal neural network; 2-D transformation; data augmentation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 C. Wu, Y. Liu and C. Hung, "Intelligent detection of serial arc fault on low voltage power lines", J. of Marine Science and Technology, Vol. 25, No. 1, pp. 43-53, 2017.
2 S. Ma, and L. Guan, "Arc fault recognition based on BP Neural Network", Int' Conf. Measuring Technology and Mechatronics Automation, 2011.
3 H. Yuanhang, Y. Wang, D. Enyuan, and Z. Jiyan, "Aviation arc fault diagnosis based on weight direct determined neural network", Int. Conf. Electric Power Equipment, 2013.
4 S. Hong, T. Kim, and S. Lee, "Study of series-arc detection algorithm", KIEE Summer Conf., 2018.
5 N. Perera and A. Rajapakse, "Recognition of fault transients using a probabilistic neural network classifier", IEEE Trans. Power Delivery, Vol. 25, Iss. 1, 2011.
6 Z. Chen and W. Li, "Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network", IEEE Tran. Inst. and Measure., Vol. 66, No. 7, July 2017.
7 H. Gu, F. Zhang, Z. Wang, Q. Ning, and S. Zhang, "Identification method for low-voltage arc fault based on the loose combination of wavelet transformation and neural network", Power Eng. and Auto. Conf., 2012.
8 P. Muller, S. Tenbohlen, R. Maier, and M. Anheuser, "Characteristics of series and parallel low current arc faults in the time and frequency domain", Proc. of the 56th IEEE Holm Conf. Electrical Contacts, 2010.
9 G. Yunmei, W. Li, W. Zhuoqi, and J. Binfeng, "Wavelet packet analysis applied in detection of low-voltage DC arc fault", IEEE Industrial Electronics and Applications, 2009.
10 S. Lee, C. Choi, and M. Kim, "CNN-based people recognition for vision occupancy sensors", Journal of Broadcast Engineering, Vol. 23, No. 2, March 2018, pp. 274-282.   DOI
11 E. Kim and W. Kim, "Face anti-spoofing based on combination of luminance and chrominance with convolutional neural networks", Journal of Broadcast Engineering, Vol. 24, No. 6, pp. 1113-1121, Nov. 2018.   DOI
12 T. Um, F. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kulic, "Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks", 19th ACM International Conference on Multimodal Interaction (ICMI), Nov. 2017, Glasgow, UK.