Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System

Yan Jiasheng; Sui Yang; Dai Tao; Liu Jiayi; Jin Yi; Jia Xiaolong

doi:10.13832/j.jnpe.2024.060034

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 282-292

Yan Jiasheng, Sui Yang, Dai Tao, Liu Jiayi, Jin Yi, Jia Xiaolong. Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System[J]. Nuclear Power Engineering, 2025, 46(2): 282-292. doi: 10.13832/j.jnpe.2024.060034

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Yan Jiasheng, Sui Yang, Dai Tao, Liu Jiayi, Jin Yi, Jia Xiaolong. Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System[J]. Nuclear Power Engineering, 2025, 46(2): 282-292. doi: 10.13832/j.jnpe.2024.060034

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Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System

doi: 10.13832/j.jnpe.2024.060034

Yan Jiasheng^{1, 2
,},
Sui Yang^{1, 2, 3
,
,},
Dai Tao^{1, 2},
Liu Jiayi^{1, 2},
Jin Yi^{1, 2},
Jia Xiaolong^{1, 2}

1.
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China
2.
Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang, Hunan, 421001, China
3.
Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, 350300, China

Received Date: 2024-06-27
Rev Recd Date: 2024-09-29

Available Online: 2025-01-15

Publish Date: 2025-04-15

Abstract

Abstract

Although artificial intelligence technology has been extensively employed in the field of accident diagnosis for nuclear power plants, conventional models often suffer from shortcomings such as insufficient accuracy and poor generalizability, which fail to meet the stringent requirements for accident diagnosis of the nuclear reactor coolant system (NRCS). This study establishes a new intelligent accident diagnosis model for NRCS. Firstly, to enhance the accuracy of accident diagnosis, an NRCS accident diagnosis model (CNN-GRU) integrating convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed; Firstly, to enhance the diagnostic accuracy of the model, convolutional neural networks (CNN) and gated recurrent unit (GRU) were integrated. The powerful feature extraction capabilities of CNN and the efficient time-series data classification abilities of GRU were combined to establish the NRCS accident diagnosis model (CNN-GRU). Secondly, to enhance the generalizability of the model, the grey wolf optimizer (GWO) algorithm was used to adaptively optimize the hyperparameters within the CNN-GRU model, thereby establishing the NRCS intelligent accident diagnosis model (GWO-CNN-GRU). Finally, to validate the performance of the proposed model, the NRCS in personal computer transient analyzer (PCTRAN) was used as the object of study, and the diagnostic process of one normal operating condition and four typical accident conditions was simulated. The results demonstrated that the proposed model achieved an average accident diagnosis accuracy of 99.6% on the NRCS test set for the CPR1000 reactor type, which is an improvement of 2.1% and 1.5% compared to the GRU and CNN-GRU models, respectively; Similarly, on the NRCS test set for the AP1000 reactor type, the proposed model achieved an average accident diagnosis accuracy of 99.5%, representing an increase of 1.7% and 1.3% over the other two models, respectively. Therefore, the model proposed in this paper demonstrates superior performance in terms of accuracy and generalizability, providing a valuable reference for intelligent accident diagnosis of NRCS.

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References(23)

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