Human electrocution recognition method based on residual network

Guocheng Lin; Qilin Zhou; Zhiyong Liu; Jie Han; Guokai Liang; Yuchang Ling

doi:10.1117/12.3024475

13 May 2024 Human electrocution recognition method based on residual network

Guocheng Lin, Qilin Zhou, Zhiyong Liu, Jie Han, Guokai Liang, Yuchang Ling

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131599I (2024) https://doi.org/10.1117/12.3024475
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Human electrocution presents a substantial threat to public safety. Prevailing techniques for detecting human electrocution, however, suffer from limited accuracy and extended detection timeframes. To address these challenges, we introduce a method for human electrocution detection based on a Residual Network 18 (ResNet18). First and foremost, we analyze the transient amplitude disparities and steady-state phase distinctions in residual current in the case where human electrocution and resistive leakage occur, incorporating the International Electrotechnical Commission (IEC) human impedance model with the low-voltage distribution network topology. Subsequently, we investigate a feature extraction method for fault detection based on fault phase voltage and residual current within a 2-millisecond window after the fault occurrence, resulting in a trajectory-based human electrocution detection method rooted in voltage-current patterns. On these grounds, the sampled signals traversing fault initiation and fault phase selection components are utilized to construct voltage-current trajectories, which act as inputs for ResNet18 during model training or fault identification. In closing, we validate the proposed algorithm through simulation models implemented in Matlab/Simulink, illustrating its superior identification accuracy and substantially reduced detection time when compared to existing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guocheng Lin, Qilin Zhou, Zhiyong Liu, Jie Han, Guokai Liang, and Yuchang Ling "Human electrocution recognition method based on residual network", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131599I (13 May 2024); https://doi.org/10.1117/12.3024475

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KEYWORDS

Resistance

Detection and tracking algorithms

Deep learning

Neural networks

Artificial neural networks

Feature extraction

Skin

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