Title :
Identifying of Hydraulic Turbine Generating Unit Model Based on Neural Network
Author :
Xiao, Zhihuai ; Wang, Shuqing ; Zeng, Hongtao ; Yuan, Xiaohui
Author_Institution :
Coll. of Power & Mech. Eng., Wuhan Univ.
Abstract :
It is difficult to describe hydraulic turbine generating unit system via accurate mathematics model because it is a complicated non-liner system. In the paper, RBF neural networks models were established to identify hydraulic turbine generating unit. In RBF networks training, a practical learning algorithm was proposed for adjusting effectively the node number, centers and width of Gaussian function of hidden layer nodes. Off-line training and on-line identifying were combined together to train networks and identify hydraulic turbine generating unit system. Simulation results show that the designed model can well identify the characteristic of hydraulic turbine generating unit. Thus, the identifying model can lay the good foundation for study on intelligent control strategies of hydraulic turbine generating system
Keywords :
Gaussian processes; hydraulic turbines; hydroelectric generators; radial basis function networks; Gaussian function; RBF neural network; hydraulic turbine generating unit model; radial basis function network; Artificial neural networks; Character generation; Educational institutions; Hydraulic turbines; Hydroelectric power generation; Mathematical model; Neural networks; Power generation; Power system modeling; Radial basis function networks; RBF neural network; generating unit; hydraulic turbine; model identifying;
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
DOI :
10.1109/ISDA.2006.172