Research on Anomaly Detection and Correction of Nuclear Power Plant Operation Data Based on GRU-MLP

Wang Tianshu; Yu Ren; Mao Wei; Song Xiaosen; Ma Jie

doi:10.13832/j.jnpe.2023.05.0188

Volume 44 Issue 5

Oct. 2023

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Wang Tianshu, Yu Ren, Mao Wei, Song Xiaosen, Ma Jie. Research on Anomaly Detection and Correction of Nuclear Power Plant Operation Data Based on GRU-MLP[J]. Nuclear Power Engineering, 2023, 44(5): 188-194. doi: 10.13832/j.jnpe.2023.05.0188

Citation:

Wang Tianshu, Yu Ren, Mao Wei, Song Xiaosen, Ma Jie. Research on Anomaly Detection and Correction of Nuclear Power Plant Operation Data Based on GRU-MLP[J]. Nuclear Power Engineering, 2023, 44(5): 188-194. doi: 10.13832/j.jnpe.2023.05.0188

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Research on Anomaly Detection and Correction of Nuclear Power Plant Operation Data Based on GRU-MLP

doi: 10.13832/j.jnpe.2023.05.0188

Naval University of Engineering, Wuhan, 430033, China

Received Date: 2022-10-02
Rev Recd Date: 2022-12-09
Publish Date: 2023-10-13

Abstract

Abstract

In order to solve the data quality problems such as missing, drifting and jumping in the operation data collected or stored by the instrument & control system of nuclear power plant and provide more reliable input for operational data analysis and automatic controllers, a hybrid model based on Gated Recurrent Unit and Multilayer Perception (GRU-MLP) is proposed to detect and correct the abnormal operation monitoring parameters data of nuclear power plant. Firstly, the short-term prediction algorithm of operation data based on GRU model is studied to provide reference for anomaly detection and correction of operation data. Secondly, in order to improve the prediction accuracy of GRU model for the operation data containing anomalies, the real-time correction mechanism is used for optimization. Then, using the nonlinear fitting ability of MLP model, the fixed threshold used in the "prediction-anomaly detection" mechanism is optimized to the dynamic threshold, which improves the anomaly detection accuracy of the proposed method. Finally, the accuracy and feasibility of the proposed algorithm are verified through experiments based on the operation data of a certain nuclear power plant.
- Nuclear power plant,
- Data prediction,
- Data anomaly detection and correction,
- GRU,
- MLP

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References(13)

References

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