DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique
The intricate spectrum of arc faults elicited by diverse load types introduces a complex and formidable challenge in residential series arc fault detection. Series DC arc faults pose a significant concern as they can potentially instigate fire incidents and exert adverse ramifications on power syste...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10295440/ |
| _version_ | 1797635456047775744 |
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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 intricate spectrum of arc faults elicited by diverse load types introduces a complex and formidable challenge in residential series arc fault detection. Series DC arc faults pose a significant concern as they can potentially instigate fire incidents and exert adverse ramifications on power systems if left undetected. Nonetheless, their detection within practical power systems remains challenging, predominantly attributed to the meager arc current magnitude, the absence of a discernible zero-crossing interval, and the manifestation of multifarious aberrant behaviors contingent upon the diverse array of power loads and controllers. Importantly, the conventional safeguards, notably encompassing protection fuses, may exhibit inefficacy in promptly activating during the occurrence of series DC arc faults. The ramifications of undiscerned arc faults are profound, with the potential for engendering erroneous operational modes within power systems, thereby amplifying the risk of material and human casualties. In light of these exigencies, the development of an efficacious detection mechanism targeting series arc faults within DC systems becomes a paramount imperative. This research proposed a preprocessing signal to eliminate the switching noises, which could degrade the performance of artificial machine learning algorithms. The diagnosis results valid the effectiveness of the proposed diagnosis scheme for all ranges of switching frequencies. |
| first_indexed | 2024-03-11T12:21:15Z |
| format | Article |
| id | doaj.art-e642b659cab94f02bc6358a3492c30c2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T12:21:15Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e642b659cab94f02bc6358a3492c30c22023-11-07T00:01:40ZengIEEEIEEE Access2169-35362023-01-011111958411959510.1109/ACCESS.2023.332746510295440DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination TechniqueHoang-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, Mississipi State University, Starkville, MS, USAThe intricate spectrum of arc faults elicited by diverse load types introduces a complex and formidable challenge in residential series arc fault detection. Series DC arc faults pose a significant concern as they can potentially instigate fire incidents and exert adverse ramifications on power systems if left undetected. Nonetheless, their detection within practical power systems remains challenging, predominantly attributed to the meager arc current magnitude, the absence of a discernible zero-crossing interval, and the manifestation of multifarious aberrant behaviors contingent upon the diverse array of power loads and controllers. Importantly, the conventional safeguards, notably encompassing protection fuses, may exhibit inefficacy in promptly activating during the occurrence of series DC arc faults. The ramifications of undiscerned arc faults are profound, with the potential for engendering erroneous operational modes within power systems, thereby amplifying the risk of material and human casualties. In light of these exigencies, the development of an efficacious detection mechanism targeting series arc faults within DC systems becomes a paramount imperative. This research proposed a preprocessing signal to eliminate the switching noises, which could degrade the performance of artificial machine learning algorithms. The diagnosis results valid the effectiveness of the proposed diagnosis scheme for all ranges of switching frequencies.https://ieeexplore.ieee.org/document/10295440/DC arc failureswitching noise eliminationmachine learning |
| spellingShingle | Hoang-Long Dang Sangshin Kwak Seungdeog Choi DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique IEEE Access DC arc failure switching noise elimination machine learning |
| title | DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique |
| title_full | DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique |
| title_fullStr | DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique |
| title_full_unstemmed | DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique |
| title_short | DC Series Arc Failure Diagnosis Using Artificial Machine Learning With Switching Frequency Component Elimination Technique |
| title_sort | dc series arc failure diagnosis using artificial machine learning with switching frequency component elimination technique |
| topic | DC arc failure switching noise elimination machine learning |
| url | https://ieeexplore.ieee.org/document/10295440/ |
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