Research on series arc fault detection method household loads based on voltage signals
Abstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution operation, and combines bidirectional long short-term memory recurrent network (BiLSTM) with atten...
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| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12760-7 |
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| author | Bin Li Jiahui Shu Feifan Cui |
| author_facet | Bin Li Jiahui Shu Feifan Cui |
| author_sort | Bin Li |
| collection | DOAJ |
| description | Abstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution operation, and combines bidirectional long short-term memory recurrent network (BiLSTM) with attention mechanism. Firstly, a household experimental platform was built, and the line voltage signal obtained by the experiment was subjected to wavelet transform and principal component analysis (PCA) dimensionality reduction to construct a dataset. Secondly, Inception is introduced to extract the multi-level features of the samples, and the parallel output is input into BiLSTM after global max pooling layer. Then, self-attention is used to perform reinforcement learning on the hidden state vector. Finally, the output results are classified by the fully connected layer. Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. In addition, additional experiments at different sampling frequencies show that the method has good adaptability, and the identification accuracy has better performance when the sampling frequency is 10 KHZ, which has certain theoretical guiding significance for the development of the series arc fault detection device in the next step. |
| format | Article |
| id | doaj-art-e032d9ca0a43422cbedde0b36bc26997 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e032d9ca0a43422cbedde0b36bc269972025-08-20T04:01:52ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-12760-7Research on series arc fault detection method household loads based on voltage signalsBin Li0Jiahui Shu1Feifan Cui2Faculty of Electrical and Control Engineering, Liaoning Technical UniversityFaculty of Electrical and Control Engineering, Liaoning Technical UniversityFaculty of Electrical and Control Engineering, Liaoning Technical UniversityAbstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution operation, and combines bidirectional long short-term memory recurrent network (BiLSTM) with attention mechanism. Firstly, a household experimental platform was built, and the line voltage signal obtained by the experiment was subjected to wavelet transform and principal component analysis (PCA) dimensionality reduction to construct a dataset. Secondly, Inception is introduced to extract the multi-level features of the samples, and the parallel output is input into BiLSTM after global max pooling layer. Then, self-attention is used to perform reinforcement learning on the hidden state vector. Finally, the output results are classified by the fully connected layer. Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. In addition, additional experiments at different sampling frequencies show that the method has good adaptability, and the identification accuracy has better performance when the sampling frequency is 10 KHZ, which has certain theoretical guiding significance for the development of the series arc fault detection device in the next step.https://doi.org/10.1038/s41598-025-12760-7Series arc fault detectionMulti-scale parallel convolution operationWavelet transformPrincipal component analysis dimensionality reductionBidirectional long short-term memory recurrent networkSelf-attention |
| spellingShingle | Bin Li Jiahui Shu Feifan Cui Research on series arc fault detection method household loads based on voltage signals Scientific Reports Series arc fault detection Multi-scale parallel convolution operation Wavelet transform Principal component analysis dimensionality reduction Bidirectional long short-term memory recurrent network Self-attention |
| title | Research on series arc fault detection method household loads based on voltage signals |
| title_full | Research on series arc fault detection method household loads based on voltage signals |
| title_fullStr | Research on series arc fault detection method household loads based on voltage signals |
| title_full_unstemmed | Research on series arc fault detection method household loads based on voltage signals |
| title_short | Research on series arc fault detection method household loads based on voltage signals |
| title_sort | research on series arc fault detection method household loads based on voltage signals |
| topic | Series arc fault detection Multi-scale parallel convolution operation Wavelet transform Principal component analysis dimensionality reduction Bidirectional long short-term memory recurrent network Self-attention |
| url | https://doi.org/10.1038/s41598-025-12760-7 |
| work_keys_str_mv | AT binli researchonseriesarcfaultdetectionmethodhouseholdloadsbasedonvoltagesignals AT jiahuishu researchonseriesarcfaultdetectionmethodhouseholdloadsbasedonvoltagesignals AT feifancui researchonseriesarcfaultdetectionmethodhouseholdloadsbasedonvoltagesignals |