Nuclear Techniques ›› 2017, Vol. 40 ›› Issue (8): 80604-080604.doi: 10.11889/j.0253-3219.2017.hjs.40.080604

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles    

Fault diagnosis of LOCA based on ANN methods

LI Shixian1, LIU Jingquan1, SHEN Yonggang2   

  1. 1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
    2. China Nuclear Power Technology Research Institute, Shenzhen 518000, China
  • Received:2017-02-27 Revised:2017-04-19 Online:2017-08-10 Published:2017-08-11

Abstract: Background: Loss of coolant accident (LOCA) is one of the typical accidents in safety analysis of nuclear power plant and the location and the size of break will affect its treatment and consequences directly. Purpose:This study aims to diagnose the location and the size of break by using artificial neural network (ANN) based pattern recognition approach. Methods: CATHARE program was used to model and simulate different location and size of break in LOCA for the CPR1000 nuclear power system. Six types of thermal-hydraulic parameters were extracted to train four types of ANN methods (back propagation (BP) neural network, Elman neural network, radial basis function (RBF) neural network and support vector machine) and the trained ANNs were utilized to diagnose the location and the size of break. Results: The optimized support vector machine (SVM) is best method in terms of diagnosis accuracy and stability among 4 ANNs. Conclusion: The operators can obtain more detailed information about break by SVM to deal with the accident efficiently, when a LOCA happens.

Key words: Artificial neural network, LOCA, CPR1000

CLC Number: 

  • TL364+.4