Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm

Sun Yuanli; Song Zhihao

doi:10.13832/j.jnpe.2022.04.0185

Volume 43 Issue 4

Aug. 2022

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Sun Yuanli, Song Zhihao. Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm[J]. Nuclear Power Engineering, 2022, 43(4): 185-190. doi: 10.13832/j.jnpe.2022.04.0185

Citation:

Sun Yuanli, Song Zhihao. Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm[J]. Nuclear Power Engineering, 2022, 43(4): 185-190. doi: 10.13832/j.jnpe.2022.04.0185

Citation:

Sun Yuanli, Song Zhihao. Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm[J]. Nuclear Power Engineering, 2022, 43(4): 185-190. doi: 10.13832/j.jnpe.2022.04.0185

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Research on Diagnosis Method of Operational Events of Nuclear Reactor Based on Convolutional Long Short-term Memory Network and Artificial Whale Algorithm

doi: 10.13832/j.jnpe.2022.04.0185

Sun Yuanli^{1, 2
,},
Song Zhihao²

1.
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China
2.
Naval Research Academy, Beijing, 100161, China

Received Date: 2021-05-24
Rev Recd Date: 2021-07-20
Publish Date: 2022-08-04

Abstract

Abstract

In case of abnormality in the nuclear power plant, the cause shall be diagnosed in time to avoid serious consequences to the operators and the surrounding environment. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) network can better extract the local characteristics of data and the characteristics of memory time series information, and study the operational event diagnosis technology of nuclear reactor based on convolutional long short-term memory (CLSTM) network and artificial whale algorithm. The method described in this paper was tested by the simulation experiment of nuclear power plant reactor simulator, and the final test accuracy is 99.91%, which proves the effectiveness of the research method described in this paper. The relevant research results can be used as a diagnosis method of nuclear power plant operational events, which is conducive to improving the intelligence and information level of operational event diagnosis, providing a technical basis for few or no people on duty in nuclear power plants, and improving the public's understanding and trust in nuclear power plants.
- Nuclear reactor,
- Operational event diagnosis,
- Convolutional long short-term memory (CLSTM),
- Artificial whale algorithm

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References(13)

References

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