| Autor: |
Changan Ren, Jichong Lei, Jie Liu, Jun Hong, Hong Hu, Xiaoyong Fang, Cannan Yi, Zhiqiang Peng, Xiaohua Yang, Tao Yu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Energies, Vol 17, Iss 16, p 4049 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1073 |
| DOI: |
10.3390/en17164049 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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