基于径向基函数神经网络的水轮发电机组效率曲线计算方法

Calculating Efficiency of Water Turbine Generator Unit Based on Radial Basis Function Nerual Network

提出用径向基函数(RBF)神经网络进行水轮发电机组效率曲线计算的方法,并建立了径向基函数神经网络模型,以有限水头下原型效率试验数据为样本进行训练,所得的网络可快速准确地计算任意水头下的效率特性曲线。与BP神经网络模型的对比结果表明,该方法避免了BP神经网络的局部极小及收敛速度慢等缺点,在精度、训练速度等方面优于BP神经网络。

A method based on Radial Basis Function (RBF) neural network for calculating efficiency of water turbine generator unit is proposed, and the RBF neural network model is established. The RBF neural network is trained with typical onsite efficiency test data of a turbine generator unit, and then the trained RBF neural network is applied to calculating the efficiency of the unit at any water head quickly. Another neural network model based on Back Propagation Network is trained for comparison. The results show that the RBF neural network is better than BP neural network in accuracy and speed of training.