Series DC Arc Fault Detection Using Machine Learning Algorithms
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dang, H. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Kwak, SangShin | - |
dc.contributor.author | Choi, S. | - |
dc.date.accessioned | 2024-03-14T17:31:45Z | - |
dc.date.available | 2024-03-14T17:31:45Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72863 | - |
dc.description.abstract | The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current—average value, median value, variance value, RMS value, and distance of the maximum and minimum values—were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared. Author | - |
dc.format.extent | 19 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Series DC Arc Fault Detection Using Machine Learning Algorithms | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3115512 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.9, pp 133346 - 133364 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000704103000001 | - |
dc.identifier.scopusid | 2-s2.0-85115784941 | - |
dc.citation.endPage | 133364 | - |
dc.citation.startPage | 133346 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 9 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Arc Fault Detection | - |
dc.subject.keywordAuthor | Artificial Intelligence | - |
dc.subject.keywordAuthor | DC Arc Fault | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | Series Arc | - |
dc.subject.keywordPlus | Electric power systems | - |
dc.subject.keywordPlus | Fault detection | - |
dc.subject.keywordPlus | Learning algorithms | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Arc fault detection | - |
dc.subject.keywordPlus | Arc faults | - |
dc.subject.keywordPlus | DC arc fault | - |
dc.subject.keywordPlus | Fire accident | - |
dc.subject.keywordPlus | Load type | - |
dc.subject.keywordPlus | Machine learning algorithms | - |
dc.subject.keywordPlus | Machine-learning | - |
dc.subject.keywordPlus | Power | - |
dc.subject.keywordPlus | Series arc | - |
dc.subject.keywordPlus | Series arc-fault | - |
dc.subject.keywordPlus | Artificial intelligence | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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