Research on an Intelligent Fault Diagnosis Method for Small Modular Reactors

Changan Ren, Jichong Lei, Jie Liu, Jun Hong, Hong Hu, Xiaoyong Fang, Cannan Yi, Zhiqiang Peng, Xiaohua Yang () and Tao Yu ()
Additional contact information
Changan Ren: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Jichong Lei: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Jie Liu: School of Computing/Software, University of South China, Hengyang 421001, China
Jun Hong: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
Hong Hu: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
Xiaoyong Fang: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
Cannan Yi: School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
Zhiqiang Peng: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Xiaohua Yang: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
Tao Yu: School of Nuclear Science and Technology, University of South China, Hengyang 421001, China

Energies, 2024, vol. 17, issue 16, 1-15

Abstract: Small modular reactors (SMRs) are currently advancing towards increased degrees of automation and intelligence, with intelligent control emerging as a prominent trend in SMR development. SMRs exhibit significant variations in design specifications and safety auxiliary system design as compared to conventional commercial nuclear power reactors. Consequently, defect diagnostic techniques that rely on commercial nuclear power plants are not appropriate for SMRs. This study designed a defect detection system for the System-integrated Modular Advanced ReacTor SMR by utilizing the PCTRAN/SMR V1.0 software and a deep learning neural network structure. Through the comparison of several neural network designs, it was discovered that the CNN-BiLSTM model, which utilizes bidirectional data processing, obtained a fault diagnostic accuracy of 97.33%. This result confirms the accuracy and effectiveness of the fault diagnosis system. This strongly supports the eventual implementation of autonomous control for SMRs.

Keywords: SMR; PACTRAN; deep learning; CNN-BiLSTM; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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