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http://dx.doi.org/10.9723/jksiis.2022.27.6.095

Nuclear Power Plant Severe Accident Diagnosis Using Deep Learning Approach  

Sung-yeop, Kim (한국원자력연구원)
Yun Young, Choi (국가수리과학연구소)
Soo-Yong, Park (한국원자력연구원)
Okyu, Kwon (국가수리과학연구소)
Hyeong Ki, Shin (한국원자력안전기술원)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.6, 2022 , pp. 95-103 More about this Journal
Abstract
Quick and accurate understanding of the situation in a severe accident is essential for conducting the appropriate accident management and response using the accident diagnosis information. This study employed deep learning technology to diagnose severe accidents through the major safety parameters transferred from a nuclear power plant (NPP) to AtomCARE. After selecting the major accident scenarios to consider, a learning database was established for particular scenarios affiliated with major scenarios by performing a large number of severe accident analyses using MAAP5 code. The severe accident diagnosis technology, which classifies detailed accident scenarios using the major safety parameters from NPPs, was developed by training it with the established database . Verification and validation were conducted by blind test and principal component analysis. The technology developed in this study is expected to be extended and applied to all severe accident scenarios and be utilized as a base technology for quick and accurate severe accident diagnosis.
Keywords
Severe accident; Accident diagnosis; Accident management; Deep learning; Principal component analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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