基于神经网络的蒸汽发生器过程辨识方法研究

Research on Identification Method Based on Neural Network for Nuclear Steam Generator Water Level

在核电站运行过程中,由于蒸汽流量随负荷变化,蒸汽发生器内沸腾区域的气泡数量因局部压力变化而变化,水位呈现瞬时“虚假水位”现象,给蒸汽发生器的水位特性辨识带来困难。如果处理不当,就会严重影响核电站的安全运行。为了提高蒸汽发生器水位特性的辨识效果,对基于神经网络的蒸汽发生器水位辨识方法进行了研究。辨识模型采用串-并联型辨识结构。网络训练采用Levenberg-Marququardt BP学习算法(LMBP)。仿真结果表明,所提出的方法具有良好的辨识性能。

The false water level, which caused by the change of the number of steam bubbles which follows the change of local pressure due to the change of steam flow rate, make the nuclear steam generator water level difficult to identify. In order to improve the effect of identification, the identification method based on neural networks for nuclear steam generator water level is researched in this paper. The series-parallel model is applied to the identification and the back propagation algorithm of Levenberg- Marququardt type（LMBP） is employed to train the network. The proposed method with a good performance of identification is demonstrated by simulation results.