@inproceedings{ed91cf00cb354051aa56e2f964a91ea6,
title = "Research on Extracting Potential DC Arc Fault Features Based on Data Mining Methods",
abstract = "Due to the great difference in the properties of cable materials used in DC lines, the time-frequency features of DC arc fault that are easy to extract are difficult to take into account various cable materials. Therefore, it is necessary to mine potential arc fault features of different materials from the arc fault signals to meet the needs of distinguishing fault state from normal state. Firstly, the current waveforms of DC arc fault are obtained from the experiments of different electrode materials, four type of data mining methods are used to mine the potential DC arc fault information in the current signals to extract the DC arc fault detection features. Then, based on the principles of the proposed data mining methods, the mining results are compared to obtain the optimal mining features for various electrode materials. Finally, the construction of DC arc fault detection algorithm for different electrode materials is realized based on SVM model. The detection results indicate that the potential arc fault features can increase the accuracy of arc fault detection.",
keywords = "DC arc fault, PV system, data mining, detection algorithm",
author = "Hancong Wu and Shiwei Ge and Yingqing Zhou and Yu Meng and Xingwen Li and Silei Chen and Xiaoshuai Wang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 68th IEEE Holm Conference on Electrical Contacts, HOLM 2023 ; Conference date: 04-10-2023 Through 11-10-2023",
year = "2023",
doi = "10.1109/HOLM56075.2023.10352252",
language = "英语",
series = "Electrical Contacts, Proceedings of the Annual Holm Conference on Electrical Contacts",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Electrical Contacts 2023 - Proceedings of the 68th IEEE Holm Conference on Electrical Contacts, HOLM 2023",
}