Identification of Dynamics for Nuclear Steam Generator Water Level Process Using RBF Neural Networks
Identification of Dynamics for Nuclear Steam Generator Water Level Process Using RBF Neural Networks
复制标题
基于 RBF 神经网络的核蒸汽发生器水位过程动力学辨识
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Peng Wei
中科院分区：
文献类型：
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作者：
Zhou Gang;Chen Xin;Ye Wei;Peng Wei
在核蒸汽发生器（SG）运行过程中，由于逆热力效应的存在，使得SG水位过程动态特性难以辨识。为了提高辨识效果，提出了一种基于径向基函数（RBF）神经网络（ANN）的辨识方法。辨识模型采用串并联模型，保证了辨识过程的收敛性和稳定性。RBF神经网络的训练算法采用正交最小二乘法。利用秦山核电厂蒸汽发生器的数学模型进行了仿真验证。通过对蒸汽流量和给水流量阶跃变化的蒸汽发生器典型工况的辨识，验证了基于径向基函数网络的蒸汽发生器过程动态建模的可行性。辨识结果表明，采用径向基函数网络能够正确辨识SG过程动态特性，且具有足够的精度和较快的收敛速度。
In the operation of nuclear steam generator (SG), the reverse thermal-dynamic effects make SG water level process dynamics characteristic difficult to identify. In order to improve the effect of identification, a new method based on radial basis function (RBF) neural networks (ANN) is proposed and investigated in this paper. The identification model employs series-parallel model to assure the convergence and stability of identification process. The train algorithm for the RBF neural network (RBFN) adopts the orthogonal least square (OLS) method. The mathematical model of the SG in Qinshan Nuclear Power Plant (NPP) in China is used for simulation demonstration. The identification on SG typical operation modes, which the steam flow rate and feed water flow rate are step change respectively, were implemented to demonstrate the feasibility of modeling SG process dynamics employing RBFN. The identification results show that employing RBFN can identify SG process dynamics correctly and has adequate precision and fast convergence.