Acknowledgement
This work was supported by the Korea Electric Power Corporation (Grant number: R21XA01-3) and "The Competency Development Program for Industry Specialists" of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by the Korea Institute for Advancement of Technology (KIAT) (No. P0012453, Next-generation Display Expert Training Project for Innovation Process and Equipment, Materials Engineers).
References
- Miao, W., Xu, Q., Lam, K.H., Pong, P.W.T., Poor, H.V.: DC arc fault detection based on empirical mode decomposition of arc signatures and support vector machine. IEEE. Sensors. 21(5), 7023-7033 (2021) https://doi.org/10.3390/s21217023
- Chen, S., Li, X., Xiong, J.: Series arc fault identification for photovoltaic system based on time-domain and time-frequency-domain analysis. IEEE. Photovoltaics. 7(4), 1105-1114 (2017) https://doi.org/10.1109/JPHOTOV.2017.2694421
- Johnson, J., Pahl, B., Luebke, C., Pier, T., Miller, T., Strauch, J., Kuszmaul, S., Bower, W.: Photovoltaic DC-arc-fault detector testing at sandia national laboratories. In: IEEE Photovoltaic Specialists Conference Seattle, WA, USA, 003614-003619 (2011)
- Dini, D. A., Brazis, P. W., Yen, K. H.: Development of arc-fault-circuit interrupter requirements for photovoltaic system. In: IEEE Photovoltaic Specialists Conference Seattle, WA, USA, 001790- 001794 (2011)
- UL1699B: Standard for photovoltaic DC arc-fault circuit protection. underwriters laboratories (2018)
- Naidu, M., Schoepf, T.J., Gopalakrishnan, S.: Arc fault detection scheme for 42-V automotive DC networks using current shunt. IEEE. Trans. Power Electron. 21(3), 633 (2006)
- Streck, M., Nothnagel, F., Berger, F.: Parameters' Values of small signal equivalent circuit of electric arc gaps in DC network, pp. 1-6. IEEE. Int. Power Engineering. Conf, Stoke on Trent, UK (2015)
- Kim, J.C., Kwak, S.S.: Frequency-Domain Characteristics of Series DC Arcs in Photovoltaic Systems with Voltage-Source Inverters. MDPI. Applied Sciences. 10(22), 8042 (2020) https://doi.org/10.3390/app10228042
- Park, H.P., Chae, S.: DC series arc fault detection algorithm for distributed energy resources using arc fault impedance modeling. IEEE. Access. 8, 179039-179046 (2020) https://doi.org/10.1109/access.2020.3027869
- Kim, J.C., Kwak, S.S., Choi, S.D.: DC series arc detection algorithm based on adaptive moving average technique. IEEE. Access. 9, 94426-94437 (2021) https://doi.org/10.1109/ACCESS.2021.3093980
- Le, V., Yao, X., Miller, C., Tsao, B.H.: Series DC arc fault detection based on ensemble machine learning. IEEE. Trans. Power Electron. 35(8), 7826-7839 (2020) https://doi.org/10.1109/tpel.2020.2969561
- Siegel, J., Pratt, S., Sun, Y., Sarma, S.E.: Real-time deep neural networks for internet-enabled arc-fault detection. Eng. Appl. Int. 74, 35-42 (2018)
- Karmakar, S., Lala, H.: Detection and experimental validation of high impedance arc fault in distribution system using empirical mode decomposition. IEEE. System. 14(3), 3494-3505 (2020) https://doi.org/10.1109/JSYST.2020.2969966
- LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436-444 (2015) https://doi.org/10.1038/nature14539
- Momoh, J.A., Button, R.: Design and analysis of aerspace DC arcing faults using fast fourier transformation and artificial neural network. In: IEEE Power Engineering Conference Toronto, ON, Canada (2003)
- Dahl, G. E., Sainath, T. N., Hinton G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. IEEE International Conference, Vancouver, BC, Canada, pp. 8609-8613 (2013)
- Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. Int. Conf. Artif. Intell. Stat. 15, 315-323 (2011)
- Johnson, J., Kang, J.: Arc-fault detector algorithm evaluation method utilizing prerecorded arcing signatures. In: IEEE Photovoltaic Specialists Conference, Austin, TX, USA, pp. 1-5 (2011)