Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques
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 tw...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9729730/ |
| _version_ | 1828487642985005056 |
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| author | Hoang-Long Dang Sangshin Kwak Seungdeog Choi |
| author_facet | Hoang-Long Dang Sangshin Kwak Seungdeog Choi |
| author_sort | Hoang-Long Dang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T09:54:19Z |
| format | Article |
| id | doaj.art-1198775d2d124d26bea31da8b78b4976 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T09:54:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1198775d2d124d26bea31da8b78b49762022-12-22T01:12:18ZengIEEEIEEE Access2169-35362022-01-0110260582606710.1109/ACCESS.2022.31572989729730Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent TechniquesHoang-Long Dang0https://orcid.org/0000-0002-1957-7307Sangshin Kwak1https://orcid.org/0000-0002-2890-906XSeungdeog Choi2https://orcid.org/0000-0002-7549-6093School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USAThe 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.https://ieeexplore.ieee.org/document/9729730/Artificial intelligencefault diagnosisDC parallel arc |
| spellingShingle | Hoang-Long Dang Sangshin Kwak Seungdeog Choi Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques IEEE Access Artificial intelligence fault diagnosis DC parallel arc |
| title | Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques |
| title_full | Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques |
| title_fullStr | Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques |
| title_full_unstemmed | Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques |
| title_short | Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques |
| title_sort | parallel dc arc failure detecting methods based on artificial intelligent techniques |
| topic | Artificial intelligence fault diagnosis DC parallel arc |
| url | https://ieeexplore.ieee.org/document/9729730/ |
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