Investigation on Application of Intelligent Control in SGTR Procedure

Yuan Rui; Hao Zulong; Yuan Jinxiao; Li Mingqian; Deng Shiguang

doi:10.13832/j.jnpe.2023.05.0130

Volume 44 Issue 5

Oct. 2023

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Yuan Rui, Hao Zulong, Yuan Jinxiao, Li Mingqian, Deng Shiguang. Investigation on Application of Intelligent Control in SGTR Procedure[J]. Nuclear Power Engineering, 2023, 44(5): 130-135. doi: 10.13832/j.jnpe.2023.05.0130

Citation:

Yuan Rui, Hao Zulong, Yuan Jinxiao, Li Mingqian, Deng Shiguang. Investigation on Application of Intelligent Control in SGTR Procedure[J]. Nuclear Power Engineering, 2023, 44(5): 130-135. doi: 10.13832/j.jnpe.2023.05.0130

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Investigation on Application of Intelligent Control in SGTR Procedure

doi: 10.13832/j.jnpe.2023.05.0130

1.
School of Nuclear Science and Engineering, North China Electric Power University, Beijing, 102206, China
2.
China Nuclear Power Engineering Co., Ltd. Beijing, 100037, China

Received Date: 2022-10-06
Rev Recd Date: 2022-12-07
Publish Date: 2023-10-13

Abstract

Abstract

After the occurrence of steam generator heat transfer tube rupture (SGTR) accident in nuclear power plant, operators shall follow the corresponding procedures. However, in accident sequence processing, the cooling and depressurizing effects of primary circuit coolant mostly depend on the operating experience of the operator. To solve this problem, this paper proposes a SGTR operating procedure optimization method based on LSTM and adaptive proportional-integral-differential (PID), and gives the implementation process. The SGTR accident simulation experiment was carried out by a 1000 MW simulator. Based on the simulation experiment data, the proposed method was tested. Simulation results show that, compared with manual operation, the proposed intelligent operation method has better cooling and depressurization effects. The proposed method can provide a new idea for intelligent operation of accident procedures in nuclear power plants.
- Nuclear power plant,
- SGTR,
- Operating procedure optimization,
- Long short-term memory (LSTM)

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References(7)

References

[1]	AHN J, LEE S J. Deep learning-based procedure compliance check system for nuclear power plant emergency operation[J]. Nuclear Engineering and Design, 2020, 370: 110868. doi: 10.1016/j.nucengdes.2020.110868
[2]	刘立欣,王喆. 核电厂SGTR规程优化研究[J]. 核动力工程,2022, 43(4): 126-130.
[3]	刘海宇,李力,李骜. 基于任务分析的核电厂计算机化运行规程人机界面研究[J]. 核科学与工程,2021, 41(3): 649-656.
[4]	沈学泽. 核电厂事故规程信息化系统对操纵员工作负荷的影响研究[D]. 衡阳: 南华大学, 2021.
[5]	许余,黄远远,熊立红,等. 基于本体的核电运行规程模型构建方法[J]. 核动力工程,2020, 41(5): 142-145.
[6]	KIM H, ARIGI A M, KIM J. Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder[J]. Annals of Nuclear Energy, 2021, 153: 108077. doi: 10.1016/j.anucene.2020.108077
[7]	LEE S, KIM J. Design of computerized operator support system for technical specification monitoring[J]. Annals of Nuclear Energy, 2022, 165: 108661. doi: 10.1016/j.anucene.2021.108661