机组在线运行优化系统及实时目标工况的确定

介绍了机组在线运行优化系统的体系结构、数据流程和功能模块，提出了机组运行目标工况的定义和实现方案。采用基于趋势提取的检测方法对机组历史运行工况数据库进行稳态判定，将运行不可控因素作为约束条件运用K-均值法将机组稳定运行工况聚类到不同的工况簇中，以机组供电煤耗作为评价基准对各工况簇中的工况进行寻优，将各工况簇中的运行最优工况组合起来作为训练样本，建立起机组运行目标工况的神经网络模型，在进行实例验证后对模型进行分析讨论。实际应用表明，模型能够及时跟踪机组的运行特性变化，实时确定机组目标工况，对于提高机组经济运行水平具有现实意义。

A Unit On-line Operation Optimization System and Determination of Real-time Optimum Operation Mode

The software structure, data flow and function modules of a new unit on-line operation optimization system are described. A new conceptualization of unit optimum operation mode is expatiated to define a constrained optimization problem. A steady state judgment method based on tendency distillation is used to eliminate the unstable operation mode in the unit original operation mode database. All unit stable operation modes are divided into various clusters by K-means analysis. The optimum operation mode in each cluster is obtained with the unit net coal consumption rate as an assessment standard. An artificial neural network model for unit optimum operation mode is developed and verified. An example is given to demonstrate the effectiveness of the model. The application results show that the model with better predicting performance and higher calculation speed can provide operators with an on-line unit optimum operation mode.