Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection
In this study, a novel method that progressively applies the fastest form of singular-value decomposition (SVD) to extract nonperiodic arc-fault features is proposed in order to pursue a competent solution for AC weak arc fault detection. First, bus-current signals of the normal state and the arc-fa...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9751612/ |
| _version_ | 1828784102977830912 |
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| author | Yu-Long Shen Rong-Jong Wai |
| author_facet | Yu-Long Shen Rong-Jong Wai |
| author_sort | Yu-Long Shen |
| collection | DOAJ |
| description | In this study, a novel method that progressively applies the fastest form of singular-value decomposition (SVD) to extract nonperiodic arc-fault features is proposed in order to pursue a competent solution for AC weak arc fault detection. First, bus-current signals of the normal state and the arc-fault state are collected and normalized before being processed by progressive SVD (PSVD) to detect the discrepancy brought by comparatively stronger arc-fault nonperiodic components expressed in singular values. To provide a more comprehensive feature extraction for an enhanced accuracy, the fast Fourier transform (FFT) is incorporated for accumulating periodic variations caused by arc faults. Because weak arc faults are difficult to distinguish from normal signals when they start, a double diagnostic window frame (DDWF) is designed to reduce false negative errors. The effectiveness of each partial design of the method is verified by experiments with numerous load types and current amplitudes conducted on an industrial experimental platform. The proposed PSVD-FFT algorithm has achieved a satisfactory and consistent performance measured by both the detection accuracy and diagnosis time in all of the experiments. The proposed method is on average at least 10% more accurate than the selected methods for a parallel comparison (in total more than a thousand experimental cases), with a satisfactory range of execution time. |
| first_indexed | 2024-12-11T23:25:23Z |
| format | Article |
| id | doaj.art-18f2d95da56545fe977ce9122d075db8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T23:25:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-18f2d95da56545fe977ce9122d075db82022-12-22T00:46:12ZengIEEEIEEE Access2169-35362022-01-0110397523976810.1109/ACCESS.2022.31657939751612Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault DetectionYu-Long Shen0https://orcid.org/0000-0002-3157-1952Rong-Jong Wai1https://orcid.org/0000-0001-5483-7445Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, ChinaDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanIn this study, a novel method that progressively applies the fastest form of singular-value decomposition (SVD) to extract nonperiodic arc-fault features is proposed in order to pursue a competent solution for AC weak arc fault detection. First, bus-current signals of the normal state and the arc-fault state are collected and normalized before being processed by progressive SVD (PSVD) to detect the discrepancy brought by comparatively stronger arc-fault nonperiodic components expressed in singular values. To provide a more comprehensive feature extraction for an enhanced accuracy, the fast Fourier transform (FFT) is incorporated for accumulating periodic variations caused by arc faults. Because weak arc faults are difficult to distinguish from normal signals when they start, a double diagnostic window frame (DDWF) is designed to reduce false negative errors. The effectiveness of each partial design of the method is verified by experiments with numerous load types and current amplitudes conducted on an industrial experimental platform. The proposed PSVD-FFT algorithm has achieved a satisfactory and consistent performance measured by both the detection accuracy and diagnosis time in all of the experiments. The proposed method is on average at least 10% more accurate than the selected methods for a parallel comparison (in total more than a thousand experimental cases), with a satisfactory range of execution time.https://ieeexplore.ieee.org/document/9751612/Arc faultsingular value decomposition (SVD)fast Fourier transform (FFT)support vector machine (SVM) |
| spellingShingle | Yu-Long Shen Rong-Jong Wai Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection IEEE Access Arc fault singular value decomposition (SVD) fast Fourier transform (FFT) support vector machine (SVM) |
| title | Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection |
| title_full | Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection |
| title_fullStr | Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection |
| title_full_unstemmed | Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection |
| title_short | Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection |
| title_sort | fast fourier transform enhanced progressive singular value decomposition algorithm in double diagnostic window frame for weak arc fault detection |
| topic | Arc fault singular value decomposition (SVD) fast Fourier transform (FFT) support vector machine (SVM) |
| url | https://ieeexplore.ieee.org/document/9751612/ |
| work_keys_str_mv | AT yulongshen fastfouriertransformenhancedprogressivesingularvaluedecompositionalgorithmindoublediagnosticwindowframeforweakarcfaultdetection AT rongjongwai fastfouriertransformenhancedprogressivesingularvaluedecompositionalgorithmindoublediagnosticwindowframeforweakarcfaultdetection |