组织协同进化的数据挖掘方法在确定凝汽器最佳压力中的应用  

Application of Data Mining by Organizational Co-evolution Algorithm in Determination of Optimal Condenser Pressure

作　　者：孙群丽[1] 马育苗[2] 

机构地区：[1]华北电力大学,保定071003 [2]保定华电电力设计研究院有限公司,保定071000

出　　处：《发电设备》2013年第4期240-243,共4页Power Equipment

摘　　要：凝汽器压力对于汽轮机运行经济性和安全性均有重大影响。针对影响凝汽器压力的因素较多,并且相互之间具有较大的耦合性,使得确定凝汽器压力最佳值有一定困难,因而提出采用数据挖掘来确定凝汽器压力最佳值的方法。利用某300MW机组凝汽器实际运行的数据,分析了影响凝汽器压力的因素,简化了计算方法,并对该方法进行了检验。结果表明:采用数据挖掘方法确定的凝汽器压力最佳值能够提高机组的效率,同时为凝汽器压力升高故障的诊断打下基础。Condenser pressure has significant impacts on both the economical efficiency and security of turbine operation. Since the condenser pressure may be affected by many factors that are strongly coupled with one another, its optimal value is difficult to determine, therefore a method of data mining was proposed. By using actual operating data of the condenser in a 300 MW unit, factors affecting the condenser pressure were analyzed, and corresponding calculation method was simplified and verified. Results show that the optimal value of condenser pressure determined by data mining method can improve the unit efficiency, which therefore lays a foundation for diagnosis of condenser pressure rise faults.

关 键 词：凝汽器 压力 组织协同进化 数据挖掘 

分 类 号：TK312[动力工程及工程热物理—热能工程]