Fault diagnosis of LOCA based on ANN methods

Shixian LI; Jingquan LIU; Yonggang SHEN

doi:10.11889/j.0253-3219.2017.hjs.40.080604

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Fault diagnosis of LOCA based on ANN methods

NUCLEAR ENERGY SCIENCE AND ENGINEERING | 更新时间：2021-05-19

- Fault diagnosis of LOCA based on ANN methods
- NUCLEAR TECHNIQUES Vol. 40, Issue 8, Article number: 080604(2017)
- 作者机构：
  
  1.清华大学工程物理系北京 100084
  2.中广核研究院有限公司深圳 518000
- 作者简介：
- 基金信息：
- DOI：10.11889/j.0253-3219.2017.hjs.40.080604
  CLC： TL364.4
- Received：27 February 2017，
  
  Revised：19 April 2017，
  
  Published：10 August 2017
- 稿件说明：
移动端阅览
Shixian LI, Jingquan LIU, Yonggang SHEN. Fault diagnosis of LOCA based on ANN methods[J]. Nuclear techniques, 2017, 40(8): 080604
DOI：

Shixian LI, Jingquan LIU, Yonggang SHEN. Fault diagnosis of LOCA based on ANN methods[J]. Nuclear techniques, 2017, 40(8): 080604 DOI： 10.11889/j.0253-3219.2017.hjs.40.080604.

摘要

冷却剂丧失事故（Loss of Coolant Accident，LOCA）是核电厂安全分析中的一类典型事故，不同的破口位置和破口尺寸将直接影响到事故的处置和后果。为判断LOCA事故的破口位置和尺寸，可以借助于神经网络的模式识别功能。针对CPR1000核电系统，利用CATHARE软件建模并仿真不同破口位置和尺寸的LOCA事故，提取事故发生时的6类热工水力参数对BP（Back Propagation）神经网络、Elman神经网络、RBF（Radial Basis Function）神经网络和支持向量机进行训练，再将训练后的神经网络用于破口位置和尺寸的诊断。结果表明，在4种神经网络中，参数优化后的支持向量机对破口位置和尺寸的诊断准确率较高且诊断稳定性较好。在LOCA事故发生时，可以利用支持向量机获取破口的详细信息，辅助操纵员高效地处理事故。

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.

关键词

Keywords

references

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