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Lee, Seung Jun
Risk Assessment and Risk-Informed Application
Research Interests
  • Probabilistic Safety Assessment, Digital I&C System Reliability, Human Reliability Analysis

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Abnormality Diagnosis Model for Nuclear Power Plants Using Two-Stage Gated Recurrent Units

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Title
Abnormality Diagnosis Model for Nuclear Power Plants Using Two-Stage Gated Recurrent Units
Author
Kim, Jae MinLee, GyuminLee, ChangyongLee, Seung Jun
Issue Date
2020-09
Publisher
KOREAN NUCLEAR SOC
Citation
NUCLEAR ENGINEERING AND TECHNOLOGY, v.52, no.9, pp.2009 - 2016
Abstract
A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC.
URI
https://scholarworks.unist.ac.kr/handle/201301/31135
URL
https://www.sciencedirect.com/science/article/pii/S1738573319309842?via%3Dihub
DOI
10.1016/j.net.2020.02.002
ISSN
1738-5733
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NUE_Journal Papers
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