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

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

更新时间：2025-09-28

- Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpyDouble hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy
- Thermal Power Generation Vol. 43, Issue 7, Pages: 36-40(2014)
- 作者机构：
  
  1. College of Mechanical Engineering
  2. Yanching Institute of Technology
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014
- 稿件说明：
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Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpyDouble hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy[J]. Thermal power generation, 2014, 43(7): 36-40.
DOI：

Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpyDouble hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy[J]. Thermal power generation, 2014, 43(7): 36-40. DOI：

摘要

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.

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