Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enth

• 作 者: GONG Huanchun
• 机 构: College of Mechanical Engineering,Yanching Institute of Technology,
• 出 处: 热力发电
• 发表日期: 2014年07期
• 关键词: steam turbine exhaust enthalpy RBF process neural network with two hidden layers

In order to diagnose the unit economic performance online,the radial basis function(RBF)process neural network with two hidden layers was introduced to online prediction of steam turbine exhaust enthalpy.Thus,the model reflecting complicated relationship between the steam turbine exhaust enthalpy and the relative operation parameters was established.Moreover,the enthalpy of final stage extraction steam and exhaust from a 300 MW unit turbine was taken as the example to perform the online calculation.The results show that,the average relative error of this method is less than 1%,so the accuracy of this algorithm is higher than that of the BP neutral network.Furthermore,this method has advantages of high convergence rate,simple structure and high accuracy.

In order to diagnose the unit economic performance online, the radial basis function (RBF) process neural network with two hidden layers was introduced to online prediction of steam turbine exhaust enthalpy. Thus, the model reflecting complicated relationship between the steam turbine exhaust enthalpy and the relative operation parameters was established. Moreover, the enthalpy of final stage extraction steam and exhaust from a 300 MW unit turbine was taken as the example to perform the online calculation.The results show that, the average relative error of this method is less than 1 %, so the accuracy of this algorithm is higher than that of BP neutral network. Futurerther, this method has advantages of high convergence rate, simple structure and high accuracy.