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11 October 2023 Application of long-short term memory network in mine leakage protection line selection
Guodong Zhang, Fuqiang Yao, Haitao Pu, Shuaishuai Zhang
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Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003B (2023) https://doi.org/10.1117/12.3004065
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China
Abstract
The single-phase grounding of underground cables (feeder lines) in coal mines causes leakage, which poses great risks to safety production. A method for selecting faulty lines using long- and short-term memory neural network (LSTM) is proposed to address this issue. Firstly, a model of the coal mine underground power supply system is built, and a large number of simulation data are generated for training the neural network. Secondly, the LSTM neural network model is built and trained by using the keras platform. Finally, the trained LSTM neural network is tested using the new simulation data generated in various operating conditions. The results indicate that the LSTM neural network can quickly and effectively distinguish the faults of bus or line and select the faulty line correctly. This method is not affected by voltage phase angle, grounding resistance value, voltage level, number of feeder lines, and intermittent arc, thus proving its effectiveness.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Guodong Zhang, Fuqiang Yao, Haitao Pu, and Shuaishuai Zhang "Application of long-short term memory network in mine leakage protection line selection", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003B (11 October 2023); https://doi.org/10.1117/12.3004065
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KEYWORDS
Neural networks
Education and training
Data modeling
Power supplies
Computer simulations
Mathematical modeling
Signal processing
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