Abnormal Event Detection in Nuclear Power Plants via Attention Networks

Tianhao Zhang (), Qianqian Jia, Chao Guo and Xiaojin Huang
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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
Qianqian Jia: 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
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, 2023, vol. 16, issue 18, 1-16

Abstract: Ensuring the safety of nuclear energy necessitates proactive measures to prevent the escalation of severe operational conditions. This article presents an efficient and interpretable framework for the swift identification of abnormal events in nuclear power plants (NPPs), equipping operators with timely insights for effective decision-making. A novel neural network architecture, combining Long Short-Term Memory (LSTM) and attention mechanisms, is proposed to address the challenge of signal coupling. The derivative dynamic time warping (DDTW) method enhances interpretability by comparing time series operating parameters during abnormal and normal states. Experimental validation demonstrates high real-time accuracy, underscoring the broader applicability of the approach across NPPs.

Keywords: nuclear energy; abnormal event detection; neural network; attention mechanism; interpretation (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: 2023
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