Identification of Dynamics for Nuclear Steam Generator Water Level Process Using RBF Neural Networks

摘要：

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.

关键词： Nuclear steam generator water level dynamics RBF neural network identification

作者: Zhou Gang Chen Xin Ye Weicheng Peng Wei

作者单位: Department of Nuclear Energy Science and Engineering,Naval University of Engineering,Wuhan 430033,Ch 92330 unit of the PLA Navy,Qingdao 266102,China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）