CAJ | 학술논문

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。
재선궤조열력계통성능계산중，기륜궤적배기통상처우습증기구，배기간도목전무법실현직접측량。대차，장신경망락방법응용우기륜궤배기함적고산，통과분석기륜궤배기함적영향인소，병대수거진행무량강화처리，대BP신경망락재불동훈련함수하적계산정도여속도，이급BP신경망락여RBF신경망락계산배기함적준학도진행비교。결과표명：BP 신경망락대훈련함수적의뢰정도교대，부분함수재계산중수궤성교강、계산시간교장；traingdx、trainscg화trainoss 3개함수계산시간교단、계산정도교고，가작위훈련함수；RBF신경망락적계산오차교BP신경망락대，단기자괄응능력강，대훈련함수적의뢰정도교소，재훈련양본족구다시，가이감소기계산오차。
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.