Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model

Meijin Lin (), Yuliang Luo, Senjie Chen, Zhirong Qiu and Zibin Dai
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Meijin Lin: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Yuliang Luo: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Senjie Chen: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Zhirong Qiu: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China
Zibin Dai: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, China

Mathematics, 2024, vol. 12, issue 24, 1-15

Abstract: Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%.

Keywords: deep learning; fault diagnosis; low-voltage power grid; attention mechanism; biological electric shock (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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