神经网络算法在汽轮机排汽焓估算中的应用

摘要

The dryness of turbine's exhaust steam can hardly be obtained via direct measurements in online performance calculation of thermal system,for the steam is usually in wet state.The artificial's neural net-works was established to calculate the exhaust enthalpy of steam turbine.By analyzing the main factors af-fecting the exhaust enthalpy and transforming the original data into non-dimensional parameters,the calcu-lation speed and accuracy were compared by using different training functions in Back-Propagation (BP) neural network.Meanwhile,the accuracy of the BP Neural Network and the Radial Basis Function (RBF) network was compared as well.Consequently,when calculating the exhaust enthalpy,the BP neural net-work depends strongly on training functions,some of which will result in strong random and low calcula-tion speed.After the research,three of the training functions:'traingdx','trainscg'and'trainoss',have the ad-vantages of both fast calculation speed and high accuracy.The RBF network has the advantages of faster calculation speed and weaker dependence on training functions but the disadvantage of lower accuracy,com-pared with the BP neural network.The error will be diminished when enough training samples are provid-ed.%在线机组热力系统性能计算中，汽轮机的排汽通常处于湿蒸汽区，排汽干度目前无法实现直接测量。对此，将神经网络方法应用于汽轮机排汽焓的估算，通过分析汽轮机排汽焓的影响因素，并对数据进行无量纲化处理，对BP神经网络在不同训练函数下的计算精度与速度，以及BP神经网络与RBF神经网络计算排汽焓的准确度进行比较。结果表明：BP 神经网络对训练函数的依赖程度较大，部分函数在计算中随机性较强、计算时间较长；traingdx、trainscg和trainoss 3个函数计算时间较短、计算精度较高，可作为训练函数；RBF神经网络的计算误差较BP神经网络大，但其自适应能力强，对训练函数的依赖程度较小，在训练样本足够多时，可以减小其计算误差。