Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

被引：14
作者：
Dang, Hoang-Long [1 ]
Kwak, Sangshin [1 ]
Choi, Seungdeog [2 ]
机构：
[1] Chung Ang Univ, Sch Elect & Elect Engn, Seoul 06974, South Korea
[2] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS 39762 USA
来源：
IEEE ACCESS | 2022年 / 10卷
基金：
新加坡国家研究基金会;
关键词：
Artificial intelligence; Circuit faults; Voltage; Inverters; Generators; Wiring; Support vector machines; fault diagnosis; DC parallel arc; FAULT-DETECTION; CLASSIFICATION MODEL; MACHINE; DIAGNOSIS; ALGORITHM;
D O I：
10.1109/ACCESS.2022.3157298
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The unwanted electric discharge usually relates to arc phenomena between two connectors. The energy from an arc might fuse the electric wiring and be responsible for a fire. Various researches have been investigated for safety operations to improve detected techniques for arc diagnosis. There are two types of arc faults: parallel and series arcs. A parallel arc happens among two electrical lines, or line and ground, due to degrading insulation or contamination. On the other hand, a series arc might result from releasing connections in the wiring. The system's current can be significantly increased by parallel arc fault compared with the series arc. In this work, the electrical behavior of the system is investigated during parallel arc faults to understand the arcing characteristics from different cases, identify electrical characteristics that are useful and reliable for the diagnosis process, and determine the location of the fault based on current or voltage of the faulted system. Eight learning techniques are adopted to detect arc fault in this study. Parallel arc signals were analyzed in the time and frequency domains, and unique characteristics of the current are extracted using Fourier analysis as an indicator for diagnosing an arc fault. This research can be used to improve arc-fault detector reliability and robustness.
引用
收藏
页码：26058 / 26067
页数：10
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