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上海电力学院学报:2019,35(6):535-538
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基于改进ARMA预测模型的电厂风机状态预测
(1.华电江苏能源有限公司句容发电分公司;2.上海电力学院)
State Prediction of Power Plant Fans Based on Improved ARMA
(1.Huadian Jiangsu Energy Co., Ltd., Jurong Power Generation Branch, Zhenjiang 212400, China;2.Shanghai University of Electric Power, Shanghai 200090, China)
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投稿时间:2019-09-17    
中文摘要: 随着1 000 MW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。以某电厂的HU27046-221型引风机为例进行分析研究,提出了一种基于改进自回归滑动平均(ARMA)预测模型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,与传统的ARMA预测方法进行对比分析,结果表明所提出的方法预测精度较高。
Abstract:With the expansion and application of the 1 000 MW ultra-supercritical coal-fired generating unit,higher requirements are imposed on the boiler's flue gas and air system.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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彭彤宇,茅大钧,韩万里.基于改进ARMA预测模型的电厂风机状态预测[J].上海电力学院学报,2019,35(6):535-538.
PENG Tongyu,MAO Dajun,HAN Wanli.State Prediction of Power Plant Fans Based on Improved ARMA[J].Journal of Shanghai University of Electric Power,2019,35(6):535-538.