热力发电
熱力髮電
열력발전
Thermal Power Generation
2015年
10期
72-76
,共5页
凝汽器%真空%PSO%RBF%软测量%预测模型
凝汽器%真空%PSO%RBF%軟測量%預測模型
응기기%진공%PSO%RBF%연측량%예측모형
steam condenser%vacuum%PSO%RBF%soft measurement%prediction model
以聚类法的径向神经网络(RBF)为主，介绍了 RBF 的输入层、隐层和输出层之间的实现细节，给出各个部分的矩阵匹配要求，采用粒子群算法(PSO)寻找 RBF 模型中的基宽和输出层权值，并给出了具体实现过程，建立了凝汽器真空软测量模型。以300 MW机组凝汽器系统的实际运行数据为例对该模型进行训练，通过凝汽器真空预测值与实际值的对比，验证了该模型对凝汽器运行状态判断的准确性，为其故障诊断提供了参考依据。
以聚類法的徑嚮神經網絡(RBF)為主，介紹瞭 RBF 的輸入層、隱層和輸齣層之間的實現細節，給齣各箇部分的矩陣匹配要求，採用粒子群算法(PSO)尋找 RBF 模型中的基寬和輸齣層權值，併給齣瞭具體實現過程，建立瞭凝汽器真空軟測量模型。以300 MW機組凝汽器繫統的實際運行數據為例對該模型進行訓練，通過凝汽器真空預測值與實際值的對比，驗證瞭該模型對凝汽器運行狀態判斷的準確性，為其故障診斷提供瞭參攷依據。
이취류법적경향신경망락(RBF)위주，개소료 RBF 적수입층、은층화수출층지간적실현세절，급출각개부분적구진필배요구，채용입자군산법(PSO)심조 RBF 모형중적기관화수출층권치，병급출료구체실현과정，건립료응기기진공연측량모형。이300 MW궤조응기기계통적실제운행수거위례대해모형진행훈련，통과응기기진공예측치여실제치적대비，험증료해모형대응기기운행상태판단적준학성，위기고장진단제공료삼고의거。
The heat transfer characteristics of condensers and the influence of vacuum degree on unit operat-ing economy were investigated.The implementation details of input layer,hidden layer and output layer of the clustering radial basis function (RBF)neural network were introduced,and each part of the matrix matching requirements was presented.The particle swarm optimization (PSO)algorithm was applied to look for the radius and output layer weights of the RBF model,and a concrete implementation process was put forward.The condenser vacuum soft measurement model was established.Moreover,taking the practi-cal running data of a 300 MW unit as an example,data training for the above model was carried out.Com-parison between the predicted steam condenser vacuum value and the monitoring data verifies this estab-lished model can j udge the condenser operation state reasonablely.
