关联规则挖掘在电站运行优化中的运用  

The Application of Association Rule in a Power Plant Operation Optimization based on Organizational Evolutionary

作　　者：谷俊杰[1] 王为力[1] 李家川[1] 孙群丽[1] 

机构地区：[1]华北电力大学能源与动力工程学院

出　　处：《电站系统工程》2009年第1期8-10,共3页Power System Engineering

基　　金：河北省教育厅指导性项目(Z2007414)

摘　　要：大型火力发电厂的实时控制过程中,许多数据之间呈现复杂的非线性关系,而数据挖掘技术能从数据中发现知识或规则,及时分析、调整参数。该文利用数据挖掘方法确定监控参数的目标值,为火电机组耗差分析提供重要依据。介绍了数据挖掘方法的相关理论,研究并应用组织进化算法确定锅炉监控参数的目标值模型。通过分析实时运行数据,挖掘出锅炉运行主要监控参数的目标值,为目标值模型的确定提供了一个新的思路和有效方法。Complicated nonlinear relationships exist among many data in the real-time control-process of large power plant. And data-mining technology could find knowledge, analyze parameters and regulate them. It ascertained target-value by means of data-mining, which supported energy-loss analysis. It introduced relative theory on data mining, studied and applied target-value model of thermal supervised parameters in the way of Organizational Evolutionary Algorithm. By analyzing real-time operating data of thermal units, and mined the target-value models for main supervised parameters of boiler. The results supply a new idea and effective method for target-value models.

关 键 词：组织进化 目标值 数据挖掘 运行优化 锅炉 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]