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A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants

Author

Listed:
  • Haixia Gu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China
    These authors contributed equally to this work.)

  • Gaojun Liu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Jixue Li

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Hongyun Xie

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Hanguan Wen

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
    These authors contributed equally to this work.)

Abstract

Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, human errors can be detected and prevented in a timely manner. This paper proposed a Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict safety-critical parameters and their future trends. The PCTran was used to extract data for four typical faults and fault levels, and eighty-six parameters were selected as characteristic quantities. The training, validation, and testing sets were collected in a ratio of 13:3:1, and appropriate hyperparameters were used to construct the Seq2Seq neural network. Compared with conventional deep learning models, the results indicated that the proposed model could successfully solve the complex problem of the trend estimation of key system parameters under the influence of operator action factors in multiple abnormal operating conditions. It is believed that the proposed model can help operators reduce the risk of human-caused errors and diagnose potential accidents.

Suggested Citation

  • Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6310-:d:1117514
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    References listed on IDEAS

    as
    1. Bing Liu & Jichong Lei & Jinsen Xie & Jianliang Zhou, 2022. "Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning," Energies, MDPI, vol. 15(22), pages 1-15, November.
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    3. Byung-Ho Kim & Min-Jong Song & Yong-Sik Cho, 2022. "Safety Analysis of a Nuclear Power Plant against Unexpected Tsunamis," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    4. Niu, Zhibin & Wu, Junqi & Liu, Xiufeng & Huang, Lizhen & Nielsen, Per Sieverts, 2021. "Understanding energy demand behaviors through spatio-temporal smart meter data analysis," Energy, Elsevier, vol. 226(C).
    5. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
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