Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants

Li Xi; Wang Jiansheng; Yang Senquan; Xue Wei

doi:10.13832/j.jnpe.2024.090025

Volume 46 Issue 2

Apr. 2025

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Li Xi, Wang Jiansheng, Yang Senquan, Xue Wei. Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants[J]. Nuclear Power Engineering, 2025, 46(2): 222-229. doi: 10.13832/j.jnpe.2024.090025

Citation:

Li Xi, Wang Jiansheng, Yang Senquan, Xue Wei. Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants[J]. Nuclear Power Engineering, 2025, 46(2): 222-229. doi: 10.13832/j.jnpe.2024.090025

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Research on Intelligent Monitoring and Warning Algorithms for Unexpected Reactor Shutdown Events in Nuclear Power Plants

doi: 10.13832/j.jnpe.2024.090025

1.
China Nuclear Power Operation Technology Co., Ltd., Wuhan, 430070, China
2.
Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, 350300, China

Received Date: 2024-09-09
Rev Recd Date: 2024-10-23

Available Online: 2025-01-15

Publish Date: 2025-04-15

Abstract

Abstract

The detection of abnormal conditions during the operation of nuclear power plant units mainly relies on threshold alarm information from the Digital Control System (DCS), with a lack of trend analysis. This paper investigates the establishment of logical relationships between variables through event logic, and based on this, employs an Auto-Associative Neural Network (AANN) model for anomaly detection of correlated variables. Finally, it uses the Empirical Mode Decomposition (EMD) trend extraction algorithm and the Adaptive Sliding Window Holt Linear Trend (HOLT) model to predict abnormal variables. This approach can provide early warnings for shutdown and reactor trip events, enabling plant operators to detect and resolve issues earlier, thus improving the operational safety of nuclear power plants. Testing experiments were conducted using both simulated data and actual unit anomaly data. The results from real data experiments show a Mean Squared Error (MSE) of 0.1 and a Goodness of Fit (R²) of 0.99, with at least 1 hour of advance warning before shutdown actions. This confirms the accuracy and early warning capabilities of the proposed AANN-HOLT warning algorithm.
- Intelligent monitoring,
- Time series,
- Trend forecasts, Warning for reactor shutdown

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References(12)

References

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