基于工况划分的火电机组运行多目标优化  

Multi-Objective Optimization Based on Division of Working Conditions for Operation of Thermal Power Unit

作　　者：叶灵芝 贾立[1] 宋鸣程 YE Lingzhi;JIA Li;SONG Mingcheng(School of Mechanical and Electrical Engineering and Automation,Shanghai University,Shanghai 200072,China)

机构地区：[1]上海大学机电工程与自动化学院,上海200072

出　　处：《自动化仪表》2019年第5期25-30,共6页Process Automation Instrumentation

基　　金：国家自然科学基金资助项目(61773251);上海市科委创新行动计划基金资助项目(16111106300;17511109400)

摘　　要：针对火电机组运行优化过程中存在的工况变化大和目标参数最优值冲突的问题,提出了基于工况划分的火电机组运行多目标优化的方法。首先,根据机组的外部约束条件,提出了适用于工况划分的K-means改进算法,通过对初始聚类数K值和初始聚类中心的选择进行优化,得到快速、合理的机组负荷和煤质情况的工况划分结果。进而,采用多目标优化方法协调经济性指标和环保性指标的最优解,通过将NSGA-Ⅱ与理想点法结合,并使用新相似度度量方法,得到性能指标最优的运行参数。以某300 MW机组历史运行数据为试验对象,进行基于工况划分的火电机组运行多目标优化研究。仿真结果表明,所提出的K-means优化算法更快速、高效,得到的划分结果也更合理;划分后的每一工况的多目标优化结果也为实时操作提供了具体的优化运行指导。In optimization process of thermal power unit, there is problem that the working conditions change greatly and the target parameter optimal value conflicts. Thus, a multi-objective optimization based on division of working conditions for operation of thermal power plant is proposed. Firstly, according to the external constraints of the generator unit, an improved K-means algorithm suitable for division of working conditions is put forward..By optimizing the selection of initial clusters number K and initial clustering center, the fast and reasonable division result of the unit load and coal quality condition is obtained. Furthermore, the multi-objective optimization method is used to coordinate the optimal solutions between the economic indicators and the environmental protection indicators, to obtain the optimal solution. Through combining the NSGA-Ⅱ and idea point method, and using new similarity metrics method, the operating parameters with optimal performance indicators are obtained.Taking the historical operation data of a 300 MW unit as the experimental object, the research on multi-objective optimization based on division of working conditions for thermal power unit operation is performed. Simulation results show that the improved K-means algorithm proposed is faster and more efficient;and the division results are more reasonable;the multi-objective optimal result of each working condition also provides specific optimization operation guide for real time operations.

关 键 词：电站运行优化 工况划分 数据挖掘 K-MEANS算法 运行参数 多目标优化 理想点法 NSGA-Ⅱ 

分 类 号：TH39[机械工程—机械制造及自动化]