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上海电力学院学报:2019,35(6):-
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基于改进ARMA的电厂风机状态预测
彭彤宇1, 茅大钧2, 韩万里2
(1.华电江苏能源有限公司句容发电分公司;2.上海电力大学自动化工程学院)
State prediction of power plant fans based on improved ARMA
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投稿时间:2019-09-17    修订日期:2019-10-25
中文摘要: 随着1000MW超超临界燃煤发电机组的扩建应用,对锅炉的风烟系统提出了更高的要求。尤其是电厂引风机运行条件复杂多变,极易发生故障,严重影响到发电组的安全、可靠运行。本文以华电江苏某电厂的HU27046-221型引风机为例进行分析研究,提出了一种基于改进ARMA的电厂风机状态预测方法。首先采用数据挖掘理论对引风机原始数据进行相关性分析,其次采用改进ARMA方法对引风机相关状态参数进行预测,最后通过与传统的ARMA预测方法进行对比分析,表明该方法预测精度较高。
Abstract:With the expansion and application of the 1000MW ultra-supercritical coal-fired generating unit, higher requirements are imposed on the boiler's wind-smoke system. In particular, the operating conditions of the induced draft fan of the power plant are complex and variable, and it is prone to failure, which seriously affects the safe and reliable operation of the power generation group. This paper takes the HU27046-221 induced draft fan of a power plant in Huadian Jiangsu as an example for analysis and research, and proposes a state prediction method based on improved ARMA for power plant fans. Firstly, the data mining theory is used to analyze the correlation data of the induced draft fan. Secondly, the improved ARMA method is used to predict the relevant state parameters of the induced draft fan. Finally, compared with the traditional ARMA prediction method, the prediction accuracy of the method is high.
文章编号:     中图分类号:TM621    文献标志码:
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  15732631146@163.com 
  15732631146@163.com 
   
引用文本:
彭彤宇,茅大钧,韩万里.基于改进ARMA的电厂风机状态预测[J].上海电力学院学报,2019,35(6):.
.State prediction of power plant fans based on improved ARMA[J].Journal of Shanghai University of Electric Power,2019,35(6):.