Series Arc Fault Detection Based on Multimodal Feature Fusion
In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/17/7646 |
| _version_ | 1797581799518371840 |
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| author | Na Qu Wenlong Wei Congqiang Hu |
| author_facet | Na Qu Wenlong Wei Congqiang Hu |
| author_sort | Na Qu |
| collection | DOAJ |
| description | In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features. |
| first_indexed | 2024-03-10T23:12:35Z |
| format | Article |
| id | doaj.art-ada2a6f344c54214bfb7ef0c186d32ec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:12:35Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ada2a6f344c54214bfb7ef0c186d32ec2023-11-19T08:52:43ZengMDPI AGSensors1424-82202023-09-012317764610.3390/s23177646Series Arc Fault Detection Based on Multimodal Feature FusionNa Qu0Wenlong Wei1Congqiang Hu2School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaIn low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.https://www.mdpi.com/1424-8220/23/17/7646series arc faultmultimodal feature fusionmachine learningone-dimensional convolutional networkdeep residual shrinkage network |
| spellingShingle | Na Qu Wenlong Wei Congqiang Hu Series Arc Fault Detection Based on Multimodal Feature Fusion Sensors series arc fault multimodal feature fusion machine learning one-dimensional convolutional network deep residual shrinkage network |
| title | Series Arc Fault Detection Based on Multimodal Feature Fusion |
| title_full | Series Arc Fault Detection Based on Multimodal Feature Fusion |
| title_fullStr | Series Arc Fault Detection Based on Multimodal Feature Fusion |
| title_full_unstemmed | Series Arc Fault Detection Based on Multimodal Feature Fusion |
| title_short | Series Arc Fault Detection Based on Multimodal Feature Fusion |
| title_sort | series arc fault detection based on multimodal feature fusion |
| topic | series arc fault multimodal feature fusion machine learning one-dimensional convolutional network deep residual shrinkage network |
| url | https://www.mdpi.com/1424-8220/23/17/7646 |
| work_keys_str_mv | AT naqu seriesarcfaultdetectionbasedonmultimodalfeaturefusion AT wenlongwei seriesarcfaultdetectionbasedonmultimodalfeaturefusion AT congqianghu seriesarcfaultdetectionbasedonmultimodalfeaturefusion |