Detection of DC Series Arc Fault Based on VMD and ELM
With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect...
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| Veröffentlicht in: | Journal of physics. Conference series 2020-04, Vol.1486 (6), p.62037 |
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| creator | Ma, Tao Tian, Ersheng Liu, Zhenxing Liu, Shuxin Guo, Tianhong Wang, Taowei Fu, Long |
| description | With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect arc faults accurately. The characteristic signals of the resistance, capacitance and inductive load under normal conditions and arc fault conditions were collected by experiments. Then, the current data was processed by variational mode decomposition (VMD). Due to the different spectral characteristics of normal mode, arc fault mode and switching transient mode, the intrinsic mode function (IMF) under arc fault mode can be selected. Finally, according to the characteristic of determined IMF components, a new arc fault criterion was proposed for general DC arc detection. The experimental results verified that the proposed method can detect arc faults accurately. |
| doi_str_mv | 10.1088/1742-6596/1486/6/062037 |
| format | Article |
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In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect arc faults accurately. The characteristic signals of the resistance, capacitance and inductive load under normal conditions and arc fault conditions were collected by experiments. Then, the current data was processed by variational mode decomposition (VMD). Due to the different spectral characteristics of normal mode, arc fault mode and switching transient mode, the intrinsic mode function (IMF) under arc fault mode can be selected. Finally, according to the characteristic of determined IMF components, a new arc fault criterion was proposed for general DC arc detection. The experimental results verified that the proposed method can detect arc faults accurately.</description><identifier>ISSN: 1742-6588</identifier><identifier>EISSN: 1742-6596</identifier><identifier>DOI: 10.1088/1742-6596/1486/6/062037</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Artificial neural networks ; Decomposition ; Electric equipment ; Electrical fires ; Fault detection ; Load resistance ; Machine learning ; Physics</subject><ispartof>Journal of physics. Conference series, 2020-04, Vol.1486 (6), p.62037</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). 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Conference series</title><addtitle>J. Phys.: Conf. Ser</addtitle><description>With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect arc faults accurately. The characteristic signals of the resistance, capacitance and inductive load under normal conditions and arc fault conditions were collected by experiments. Then, the current data was processed by variational mode decomposition (VMD). Due to the different spectral characteristics of normal mode, arc fault mode and switching transient mode, the intrinsic mode function (IMF) under arc fault mode can be selected. Finally, according to the characteristic of determined IMF components, a new arc fault criterion was proposed for general DC arc detection. The experimental results verified that the proposed method can detect arc faults accurately.</description><subject>Artificial neural networks</subject><subject>Decomposition</subject><subject>Electric equipment</subject><subject>Electrical fires</subject><subject>Fault detection</subject><subject>Load resistance</subject><subject>Machine learning</subject><subject>Physics</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqFkF1LwzAUhoMoOKe_wYB3Qm3SNKfJ5ew2P9hQmHob0nxAx1xrsl34722pTATBc3MOnOd9D-dF6JKSG0qESGmRZwlwCSnNBaSQEsgIK47Q6LA5PsxCnKKzGNeEsK6KEeJTt3NmVzdb3Hg8LfHKhdpFPAkGz_V-s8O3OjqLu_3bcor11uLZYnmOTrzeRHfx3cfodT57Ke-TxdPdQzlZJIZlokio0LmU1GlhPHCrgTsAIUwlradaUy-55YZZyAkYVjmbm4pJUXFvQXpp2RhdDb5taD72Lu7UutmHbXdSZRwkFDxjRUcVA2VCE2NwXrWhftfhU1Gi-oxU_73qk1B9RgrUkFGnvB6UddP-WD8-l6vfoGqt72D2B_zfiS-5Q3Oj</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Ma, Tao</creator><creator>Tian, Ersheng</creator><creator>Liu, Zhenxing</creator><creator>Liu, Shuxin</creator><creator>Guo, Tianhong</creator><creator>Wang, Taowei</creator><creator>Fu, Long</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200401</creationdate><title>Detection of DC Series Arc Fault Based on VMD and ELM</title><author>Ma, Tao ; Tian, Ersheng ; Liu, Zhenxing ; Liu, Shuxin ; Guo, Tianhong ; Wang, Taowei ; Fu, Long</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3287-18a4991ea8cf65da65e6688cb9df1aa1f95d5c3d6406c3bed4cb398b5fd69f9d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Decomposition</topic><topic>Electric equipment</topic><topic>Electrical fires</topic><topic>Fault detection</topic><topic>Load resistance</topic><topic>Machine learning</topic><topic>Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Tao</creatorcontrib><creatorcontrib>Tian, Ersheng</creatorcontrib><creatorcontrib>Liu, Zhenxing</creatorcontrib><creatorcontrib>Liu, Shuxin</creatorcontrib><creatorcontrib>Guo, Tianhong</creatorcontrib><creatorcontrib>Wang, Taowei</creatorcontrib><creatorcontrib>Fu, Long</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Journal of physics. 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| subjects | Artificial neural networks Decomposition Electric equipment Electrical fires Fault detection Load resistance Machine learning Physics |
| title | Detection of DC Series Arc Fault Based on VMD and ELM |
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