Authors:
Hye Jo
;
Ho Lee
;
Ji Park
and
Man Gyun Na
Affiliation:
Department of Nuclear Engineering, Chosun University, 10, Chosundae 1-gil, Dong-gu, Gwangju, Republic of Korea
Keyword(s):
Failure Prediction, Reactor Protection System, Nuclear Power Plants.
Abstract:
Nuclear power plants (NPPs), which generate electricity through nuclear fission energy, are crucial for safe operation due to the potential risk of exposure to radioactive materials. NPPs contain a variety of safety systems, and this study aims to develop an artificial intelligence-based failure prediction model that can predict and prevent potential failures in advance by targeting the reactor protection system (RPS). Currently, failure data for RPS are being collected through a testbed, so we conducted preliminary modeling using open-source data due to insufficient data acquisition. The applied open-source data are the accelerated aging data of insulated gate bipolar transistors (IGBTs), and the remaining useful life of IGBT was predicted using long short-term memory and Monte Carlo dropout technology. Also, physical rules were applied to improve their prediction performance and their applicability was confirmed through performance evaluation. Through performance evaluation of the
developed prediction models, we explored the optimal model and confirmed the applicability of the applied methodologies and technologies.
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