Research on Method of Nuclear Power Plant Operation Fault Diagnosis Based on a Combined Artificial Neural Network
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摘要: 针对核电站运行时故障或事故状态的在线实时判定,提出了一种基于复合人工神经网络的故障诊断和事故判定方法。其基本思想是:首先应用BP网络对事故进行成组快速诊断,而后应用RBF网络对BP网络的诊断结果进行区分和检验。利用核电站正常状态和多种事故状态下各故障特征参数输出的仿真计算结果,对所提出的方法进行了检验。结果表明,通过BP网络和RBF网络的优势互补,不仅能对学习过的故障进行快速、正确的诊断,对不同工况下的故障以及未定义的新故障也能够有效地识别。该方法采用的是随时间序列输出诊断结果及其可信度的方式,操纵员容易接受推理结果。Abstract: To solve the online real-time diagnosis problem of the nuclear power plant in operating condition,a method based on a combined artificial neural network is put forward in the paper.Its main principle is: using the BP neural network for the fast group diagnosis,and then using the RBF neural network for distinguishing and verifying the diagnostic result.The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant.The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately,but also can identify the faults in different power status,as well as the unlearned faults.The outputs of the diagnosis system are in form of the reliability of the faults,and are changing with the lasting of the operation time of the plant.This makes the diagnosis results be more acceptable to operators.
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Key words:
- Nuclear power plant /
- Fault diagnosis /
- BP Neural network /
- RBF neural Network
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