Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction

Xingyu Xiao, Ben Qi, Jingang Liang (), Jiejuan Tong, Qing Deng and Peng Chen
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Xingyu Xiao: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Ben Qi: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Jingang Liang: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Jiejuan Tong: Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
Qing Deng: Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
Peng Chen: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

Energies, 2023, vol. 17, issue 1, 1-20

Abstract: In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-Lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-Lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LOCA breach size estimation.

Keywords: deep learning; breach size estimation; lattice algorithm; fault diagnosis; loss-of-coolant accident (LOCA) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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