Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical syst...
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Autor*in:	Hoang-Long Dang [verfasserIn] Sangshin Kwak [verfasserIn] Seungdeog Choi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Arc diagnosis artificial intelligence DC arc failure identifying arc fault

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 76386-76400
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:76386-76400

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3192517

Katalog-ID:	DOAJ020004281

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topic_facet	Arc diagnosis artificial intelligence DC arc failure identifying arc fault Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Hoang-Long Dang @@aut@@ Sangshin Kwak @@aut@@ Seungdeog Choi @@aut@@
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callnumber-first	T - Technology
author	Hoang-Long Dang
spellingShingle	Hoang-Long Dang misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms
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topic_title	TK1-9971 Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms Arc diagnosis artificial intelligence DC arc failure identifying arc fault
topic	misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Arc diagnosis misc artificial intelligence misc DC arc failure misc identifying arc fault misc Electrical engineering. Electronics. Nuclear engineering
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title	Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms
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title_sort	identifying dc series and parallel arcs based on deep learning algorithms
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title_auth	Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms
abstract	Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.
abstractGer	Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.
abstract_unstemmed	Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.
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title_short	Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms
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