Best estimate plus uncertainty analysis of small break loss of coolant accidents with different break sizes for CPR1000

Xiong, Qingwen; Gou, Junli; Shan, Jianqiang

doi:10.1016/j.anucene.2019.107228

Cited by 12 publications

(1 citation statement)

References 13 publications

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“…The data-driven fault diagnosis method 3,4 learns the historical data and simulation data of the marine nuclear power system through models such as neural networks 5 and support vector machines 6 , which can quickly and efficiently diagnose the size and location of the break. Scholars have carried out research on the estimation of break size 7,8 . But for the diagnosis of break size, in the intelligent diagnosis model, the classification method of break size in training data is generally with equal size interval, which means that the difference between the two adjacent types of break size is the same.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis method for the break size of marine nuclear power plant by variable interval classification and probabilistic neural networks

Liu¹,

Lei²,

Wang³

et al. 2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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If the Probabilistic Neural Networks (PNN) based on the classification method of equal size interval in training data is used to diagnose the break size of the marine nuclear power plant, the accuracy of the diagnosis results is low when the break size is small. Therefore, a diagnosis method for the break size of a marine nuclear power plant based on the classification method of variable size interval and the PNN is proposed. First, the break sizes are classified according to the variable size interval. Then the data under different break sizes is generated, and the PNN is used to learn it. Next, the corresponding operation data is generated as the real-time data. Finally, the PNN model is used to diagnose the break size. The above processes are repeated with three different variable size interval classification methods, including the break size increasing proportionally, the break size interval increasing proportionally, and the break size interval increasing by arithmetic progression. The diagnosis results are compared with the classification method of equal size interval. And finally, the different classification methods of break size are combined for analysis. The results show that the use of variable size interval classification method that the break size interval changing according to arithmetic progression can increase the accuracy of diagnosis results by 1.21%, and combining it with the classification method of equal size interval can significantly increase the accuracy by 7.21%.

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