Journal
ANNALS OF NUCLEAR ENERGY
Volume 167, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2021.108785
Keywords
Data mining of nuclear power plant; Anomaly detection; UnsVupervised deep generative model; Variational auto encoder; Isolation forest
Categories
Funding
- Science and Technology on Reactor System Design Technology Laboratory [HT-WDZC-02-202000]
- Fundamental Research Funds for the Central Universities [3072020CFT2403]
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This paper proposes an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model. The method uses Variational Auto Encoders (VAE) for data preprocessing and Isolation Forest (iForest) to identify abnormal operation data. The verification results show that the method can distinguish the normal or abnormal operation state of the nuclear power plant in real time and provide judgment basis for accident classification and subsequent rescue.
In the field of traditional industrial control, anomaly detection method is mainly used to identify data items that do not match the normal operation state of the system. The traditional machine learning algorithm needs the transient operation data of normal and accident conditions to identify the abnormal state of nuclear power plant. The transient operation data of nuclear power plant during normal condition is sufficient, but it is lacks of transient operation data in accident conditions. To solve the above problems, an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model is established by using Variational Auto Encoders (VAE) and Isolation Forest (iForest) in this paper. The biggest advantage of this method is that it can only make use of the normal operation data of the nuclear power plant to make the nuclear power plant control system effectively identify whether the current state of nuclear power plant is normal operation or in accident condition. In the unsupervised deep generative model, VAE is used for data preprocessing, and iForest is used to identify abnormal operation data. Then, the method is verified in seven variable and accident conditions, such as power reduction condition, steam generator tube rupture accident and so on. The verification results show that the anomaly detection method can recognize the current abnormal condition immediately when the accident happens. And the time consumed to identify a group of operation parameters corresponding to the operation state of the nuclear power plant is about 3 ms, which can satisfy the real-time requirements of the control system. Therefore, the anomaly detection method based on unsupervised deep generative model can distinguish the normal or abnormal operation state of the nuclear power plant in real time and effectively, and provide judgment basis for accident classification and subsequent rescue.(c) 2021 Published by Elsevier Ltd.
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