NONLINEAR FRANCIS HYDROTURBINE GENERATOR SET NEURAL NETWORK MODEL PREDICT CONTROL

摘要：

Due to the difficulty in describing the nonlinear characteristic of Francis turbine, this paper takes advantage of the powerful nonlinear approximate ability of the feed forward neural network to put up the Francis turbine neural network model (FTNNM) and the neural network identification model (NNIM) for the nonlinear Francis hydroturbine generator set (TGS) ncluding FTNNM. The neural network model predicts control (NNMPC) uses NNIM to predict future response to potential control signals of the FTGS. An optimization algorithm then computes the control signals that optimize future FTGS performance. The Levenberg-Marquardt algorithm is used to train the FTNNM and the NNIM. The convergence speed of the offline training is fast and the accuracy of the model is high. The neural network model FTNNM reflect nonlinear characteristic of the Francis turbine truly and the neural network identification model NNIM reflect the nonlinear relationship between the inputs and outputs of the FTGS. Simulation results show that NNMPC is an effective tool for the nonlinear FTGS including FTNNM.

关键词： Nonlinear Francis hydroturbine generator set Francis turbine neural network model Neural network identification Neural network model predict control Levenberg-Marquardt algorithm

作者: JIANG CHANG YAN PENG

作者单位: Department of Automation, Shenzhen Polytechnic, Shenzhen 518055, China Industry Center, ShenZhen Polytechnic, Shenzhen 518055, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2963-2968

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）