基于BP神经网络的汽轮机排汽干度在线计算

BP neural network based online prediction of steam turbine exhaust dryness

对于大型凝汽式汽轮机,其汽轮机末级的排汽状态点处于湿蒸汽区,由于缺乏实时有效的测量手段,汽轮机末级排汽干度难以直接确定,其计算取值一直是火电机组热力系统热经济性在线分析的难点。利用BP神经网络的非线性映射能力,以某台N1000-25/600/600型汽轮机为研究对象,建立了汽轮机末级排汽干度(排汽干度)与机组负荷和汽轮机末级排汽压力(排汽压力)的BP神经网络关系模型。将该模型通过典型工况的学习训练及校验后,用于排汽干度的全工况在线计算。结果表明,所建模型最大训练误差和最大校验误差分别为-0.006 1和-0.001 0,满足工程计算的精度要求,可用于排汽干度在线计算和预测。

In large scale condensing turbine unit,the exhaust status always lies in wet steam area.Due to the lack of effective measuring method,the exhaust dryness of the steam turbine is difficult to obtain directly,which has been the difficult problem in online economic analysis for thermal power units.By taking an N1000-25/600/600ultra-supercritical steam turbine as an example,the nonlinear mapping ability of BP neural network was used to establish a model which can reflect the relationship between exhaust dryness and unit load and exhaust pressure.After learning and training under some typical conditions,this model was used for exhaust dryness online calculation under full condition.The results show the final error of the training samples and verifying samples were controlled within-0.006 1and-0.001 0,which satisfies the accuracy requirement for engineering calculation,indicating the established BP neural network can be used in exhaust dryness prediction.