火电机组AGC指令特性分析  

Characteristic analysis of AGC signals in thermal power unit

作　　者：王瑞琪[1] 刘吉臻[1] 高萌[1] 田亮[1] 

机构地区：[1]华北电力大学新能源电力系统国家重点实验室,北京102206

出　　处：《华北电力大学学报（自然科学版）》2013年第5期84-89,共6页Journal of North China Electric Power University：Natural Science Edition

基　　金：国家重点基础研究发展计划项目(973计划)(2012CB215203);国家自然科学基金重点项目(51036002)

摘　　要：发电质量由电网调度所下发的发电任务和电厂完成情况来决定。从发电任务的角度出发,研究了AGC指令信号的特性,分别选取了Kolmogorov复杂性、样本熵和标准差三个特征量对其进行数据分析。采集某1 000 MW机组在月、日、h 3个不同时间尺度的AGC指令信号进行仿真分析,说明了所选特征量在一定程度上可以表征AGC指令的特性,对比发现样本熵对于信号复杂程度的识别能力要高于Kolmogorov复杂性。建议选取样本熵和标准差作为发电任务等级划分的依据,从而可以定量地分析电厂的发电质量。The quality of electric generated in power plants depends on the generation tasks dispatching from the power grid and the plants performance. This paper takes the tasks into account and considers the AGC signals characteristics. The Kolmogorov complexity, sample entropy and standard deviation have been chosen to analysize the signals. This method is used in one 1000 MW unit in multi-scales such as months, days and hours and the results illustrate the se- lected characteristics can quantitatively analysize the AGC signals in a certain extent, meanwhile the data analysis shows that sample entropy has better recognition ability than the Kolmogorov complexity in signal complexity analysis. We suggest to pick sample entropy and standard deviation as the basis of the grading divisiory in generation tasks, then auantitative research in the aualitv of electric can be put forward.

关 键 词：自动发电控制 Kolmogorov复杂性 样本熵 标准差 发电任务 

分 类 号：TM621[电气工程—电力系统及自动化]