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基于复相关系数的时滞联合估计及其应用

99    2019-08-27

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作者:李海军1, 夏静1, 史恒惠1, 刘长良2,3, 王梓齐3

作者单位:1. 国家电投集团河南电力有限公司技术信息中心, 河南 郑州 450001;
2. 华北电力大学 新能源电力系统国家重点实验室, 北京 102206;
3. 华北电力大学控制与计算机工程学院, 河北 保定 071000


关键词:复相关系数;时滞联合估计;NOx排放;软测量


摘要:

针对工业过程的软测量建模,为对输入与输出变量间的时滞关系进行准确、快速地估计,提出一种基于复相关系数的时滞联合估计方法。该方法以模型输入和输出数据间的复相关系数为指标,将时滞联合估计问题转化为多维优化问题,进而对各输入变量的时滞时间进行寻优。针对火电厂脱硝系统的NOx排放软测量,基于实际的运行数据和最小二乘支持向量机算法,对所提出的方法进行验证并与其他时滞估计方法进行对比。结果表明:基于复相关系数的时滞估计方法计算速度较快,时滞估计结果较准确,能在一定程度上提高软测量模型的准确度。


Time-delay joint estimation based on multiple correlation coefficient and its application
LI Haijun1, XIA Jing1, SHI Henghui1, LIU Changliang2,3, WANG Ziqi3
1. Technical Information Center of Henan Electric Power Co., Ltd. of State Power Investment Group, Zhengzhou 450001, China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;
3. School of Control and Computer Engineering, North China Electric Power University, Baoding 071000, China
Abstract: In order to accurately and quickly estimate the time-delay relationship between input and output variables, a joint time-delay estimation method based on multiple correlation coefficient is proposed for the soft sensor modeling of industrial processes. This method takes the multiple correlation coefficients between the input and output data of the model as the index, transforms the time-delay joint estimation problem into a multi-dimensional optimization problem and then optimizes the time-delay of each input variable. Based on the practical operation data and the least square support vector machine, the proposed method was verified and compared with other time-delay estimation methods for the soft sensor of NOx emission in the thermal power plant denitrification system. The results show that the proposed multiple correlation coefficient method is faster and more accurate, and the accuracy of soft sensor model can be improved to a certain extent.
Keywords: multiple correlation coefficient;time-delay joint estimation;NOx emission;soft sensor
2019, 45(8):140-144  收稿日期: 2018-07-16;收到修改稿日期: 2018-09-04
基金项目: 北京市自然科学基金资助项目(4182061)
作者简介: 李海军(1973-),男,河南焦作市人,工程师,研究方向为火电机组软测量、远程诊断
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