Online Anomaly Detection for Nuclear Power Plants via Hybrid Concept Drift

Jitao Li, Jize Guo, Chao Guo, Tianhao Zhang () and Xiaojin Huang
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Jitao Li: Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Jize Guo: Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Chao Guo: Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Tianhao Zhang: Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Xiaojin Huang: Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China

Energies, 2025, vol. 18, issue 17, 1-19

Abstract: Timely detection of anomalies in nuclear power plants (NPPs) is essential for operational safety, especially under conditions where process signals deviate gradually or abruptly from nominal patterns. Traditional detection methods often struggle to adapt under transient conditions or in the absence of well-labeled fault data. To address this challenge, we propose KD-ADWIN, an adaptive concept drift-detection framework designed for unsupervised anomaly detection in dynamic industrial environments. The method integrates three core components: a Kalman-based prediction module to extract smoothed signal trends, a multi-channel detection strategy combining statistical and derivative-based drift indicators, and an adaptive thresholding mechanism that tunes detection sensitivity based on local signal variability. Evaluations on a synthetic dataset show that KD-ADWIN accurately detects both abrupt and gradual drifts, outperforming classical baselines. Further validation using full-scope simulation data from a modular high-temperature gas-cooled reactor (MHTGR) demonstrates its effectiveness in identifying concept drifts under realistic actuator and sensor fault conditions.

Keywords: nuclear power plants; concept drift; anomaly detection; adaptive windowing; unsupervised learning (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: 2025
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