摘要
针对当前核动力装置事故判断采用传统阈值报警方法难以实现早期预警这一问题,提出根据状态参数的变化趋势、利用RBF神经网络良好的局部特性对核动力装置运行状态趋势进行预测的方法。对正常瞬变和小破口失水事故下运行状态趋势进行了预测,结果表明,RBF神经网络能很好地预测状态的变化,与实测值拟合较好,能实现事故的早期预警。
Considering that the fault diagnosis of nuclear power plant （NPP） adopting the traditional threshold way can hardly realize early warning, the prediction model according to the variation trend of state parameter and making use of the good local characteristic of RBF neural network for predicting the trend of NPP operating conditions was introduced. The operating condition trends under the normal transition and the small-break loss-of-coolant accident were predicted, The results show that RBF neural network can predict the parameters chane and the predicted value matches with the real value.
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2013年第11期2103-2107,共5页
Atomic Energy Science and Technology
关键词
核动力装置
运行状态
RBF神经网络
趋势预测
Key words., nuclear power plant operating condition RBF neural network trend prediction
