RESEARCH PAPER
The Research on Low Voltage Series Fault Arc Diagnosis Based on Synchrosqueezing Wavelet Transform and Convolutional Neural Network
1
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
2
Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China
These authors had equal contribution to this work
Submission date: 2025-04-20
Final revision date: 2025-08-09
Acceptance date: 2025-10-28
Online publication date: 2025-12-26
Publication date: 2025-12-26
HIGHLIGHTS
- Proposed CASCNet, a ShuffleNetV2-CatBoost hybrid for improved arc fault classification.
- First proposed SWT-based fault arc feature extraction, enhancing accuracy significantly.
- Designed a multi-branch arc detection platform collecting multi-branch arc data.
KEYWORDS
TOPICS
ABSTRACT
The operational dynamics of residential appliances impede arc fault identification, particularly in multi-branch circuits where fault signatures are obscured by load interference, increasing diagnostic complexity. To address this challenge, we develop the CASCNet diagnostic framework through synergistic integration of ShuffleNetV2's hierarchical feature learning and CatBoost's categorical processing. Firstly, synchronous compression wavelet transform converts arc signals into 2D time-frequency images to enhance clarity and reduce noise. Next, ShuffleNetV2 efficiently extracts features, optimized with a CA attention mechanism to reduce complexity and improve channel interaction. Finally, CatBoost’s robust generalization and noise resistance classify features. Experiments show this method accurately identifies series arc faults and load types, maintaining strong performance under noise, offering an effective solution.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (61601172) and the China Postdoctoral Science Foundation Program (2018M641287)
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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