摘要
蒸汽发生器(SG)水位指示仪表出现虚假指示或者丧失指示的情况时有发生,严重影响操纵员对核动力装置运行情况的判断。elman神经网络是典型的动态神经网络,在处理复杂非线性对象中能直接反映动态过程系统的特性。本文提出用elman神经网络重构蒸汽发生器水位的方法,以主蒸汽管道破口事故下重构蒸汽发生器水位为例建模求解,与仿真数据进行对比,结果表明elman神经网络对SG水位重构的相对误差小、精度高,能满足实际需要。
Falsehood or forfeiture Occurs frequently on the water level indication meters of the steam generator, which grievously affects the judgment of the operator on the nuclear-power plant. Eiman Neural Network is a typical dynamic neural network. It can reflect the characteristics of dynamic system directly, especially when dealing with the complicated non-linear object. A method that rebuilding the water level of steam generator with Elman Neural Network in the circumstance of the crevasse of primary stream pipeline appears is described in the paper. In comparison with the emulation results, it shows that Elman Neural Network can reconstruct the water level of steam generator exactly. The outcome can meet the practical needs and give guidance on the safe motion of the marine nuclear-power plant.
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2013年第3期116-119,共4页
Nuclear Power Engineering
关键词
ELMAN神经网络
SG水位
重构
仿真
Elman Neural Network, Water level of steam generator, Reconstruction, Simulation
