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

GONG Huanchun

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

Thermal Power Generation Vol. 43, Issue 7, Pages: 32-35(2014)

更新时间：2025-09-28

- Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy
- 作者机构：
  
  燕京理工学院机电工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK261
- Published：2014
- 稿件说明：
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GONG Huanchun. Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy[J]. Thermal Power Generation, 2014, 43(7): 32-35.
DOI：

GONG Huanchun. Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy[J]. Thermal Power Generation, 2014, 43(7): 32-35. DOI：

摘要

为实现机组经济性能在线诊断

将双隐层径向基神经网络方法引入汽轮机排汽焓在线预测计算

建立了汽轮机排汽焓特性与相关运行参数之间的复杂关系模型。并以某300MW机组汽轮机末级抽汽及排汽焓值为例进行了在线计算。结果表明:该方法在线预测汽轮机排汽焓值的平均相对误差小于1%

比BP神经网络的精度更高

同时具有训练速度快、结构简单、精度高等特点

是一种行之有效的预测方法。

Abstract

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 300MW 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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