Data reconciliation by two-step risk analysis of modeling

Author

Congli, Mei ; Guohai, Liu

Author_Institution

Dept. of Autom., Jiangsu Univ., Zhenjiang

fYear

2008

fDate

16-18 July 2008

Firstpage

132

Lastpage

135

Abstract

A new method for data reconciliation by risk analysis of modeling is presented in this paper. Yamarura designed an integer programming model for gross error detection and data reconciliation based on Akaike information criterion. But much computational cost is needed for its combinational nature. To reduce computation burden, a new method by two-step risk analysis of modeling is proposed. Measurement modeling risk is analyzed in the first step. Then gross error modeling analyzed based on the minimum measurement modeling risk is considered. The proposed method could effectively reduce the scale of the integer programming problem. Simulation shows the efficiency of the proposed method.

Keywords

data handling; integer programming; risk analysis; Akaike information criterion; data reconciliation; gross error detection; integer programming model; minimum measurement modeling risk; two-step risk analysis; Automation; Computational efficiency; Computational modeling; Error analysis; Instruments; Iterative methods; Linear programming; Risk analysis; Steady-state; Testing; Akaike information criterion (AIC); Data reconciliation; Gross error; Mixed integer optimization; Risk analysis of modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2008. CCC 2008. 27th Chinese

Conference_Location

Kunming

Print_ISBN

978-7-900719-70-6

Electronic_ISBN

978-7-900719-70-6

Type

conf

DOI

10.1109/CHICC.2008.4605669

Filename

4605669