Anomaly Detection of Core Self-Powered Neutron Detector Based on Twin Model

Chen Jing; Lu Yanzhen; Jiang Hao; Lin Weiqing; Xu Yong

doi:10.13832/j.jnpe.2023.03.0210

Volume 44 Issue 3

Jun. 2023

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Chen Jing, Lu Yanzhen, Jiang Hao, Lin Weiqing, Xu Yong. Anomaly Detection of Core Self-Powered Neutron Detector Based on Twin Model[J]. Nuclear Power Engineering, 2023, 44(3): 210-216. doi: 10.13832/j.jnpe.2023.03.0210

Citation:

Chen Jing, Lu Yanzhen, Jiang Hao, Lin Weiqing, Xu Yong. Anomaly Detection of Core Self-Powered Neutron Detector Based on Twin Model[J]. Nuclear Power Engineering, 2023, 44(3): 210-216. doi: 10.13832/j.jnpe.2023.03.0210

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Anomaly Detection of Core Self-Powered Neutron Detector Based on Twin Model

doi: 10.13832/j.jnpe.2023.03.0210

Chen Jing^{1, 2
,},
Lu Yanzhen^{1, 2},
Jiang Hao^{1, 2
,
,},
Lin Weiqing^{1, 2},
Xu Yong^{3, 4}

1.
College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
2.
Research Institute of Power System & Power Equipment, Fuzhou University, Fuzhou, 350108, China
3.
Fujian Fuqing Nuclear Power Co.,Ltd., Fuqing, Fujian, 350318, China
4.
Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

Received Date: 2022-06-23
Rev Recd Date: 2023-03-23
Publish Date: 2023-06-15

Abstract

Abstract

Self-powered neutron detector (SPND) is an important nuclear sensing device in the core, whose health status affects the safe operation of the reactor directly. Considering the measurement correlation between SPNDs at different positions in the reactor, a twin model-based anomaly detection method for SPND signals in the core was proposed in this paper. The characteristics of measurement signals of neighboring SPNDs were extracted by the Random Forest Regression (RFR) algorithm based on the historical operation data of SPND, and a twin model was built for SPNDs which outputs the same as its physical entity. Twin model and entity sensing coexisted. The residual error between the actual observation value of SPND and the twin model estimation value was calculated to serve as the anomaly detection criterion, which realized the identification and location of single-point and multi-point SPND anomalies. The experiments show that the prediction error of the twin model proposed in this paper attains the order of 1×10⁻¹⁰, which has a very high output consistency. The identification accuracy of anomaly detection can reach over 99% under various abnormal states of SPND signals, and the single-point and multi-point abnormal SPND can be accurately identified, which has a high reference value for improving the reliability and safety of the state monitoring of neutron flux measurement system in the core.
- Self-powered neutron detector (SPND),
- Twin model,
- Random forest,
- Anomaly detection

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References(15)

References

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