基于双隐层径向基过程神经网络的汽轮机排汽焓在线预测  

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

作　　者：宫唤春[1] 

机构地区：[1]燕京理工学院机电工程学院,北京065201

出　　处：《热力发电》2014年第7期32-35,共4页Thermal Power Generation

基　　金：湖南自然科学基金杰出青年项目(01jzy2102)

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。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.

关 键 词：汽轮机 排汽焓 双隐层径向基神经网络 在线预测 

分 类 号：TK212.4[动力工程及工程热物理—动力机械及工程]