Monitoring of a Thermoelectric Power Plant based on Multivariate Statistical Process Control

被引：0

作者：

Fonseca, Joyce M. F. ^{[1
]}

Sousa, Bruno M. ^{[1
]}

Aguiar, Webber E. ^{[2
]}

Braga, Anisio R. ^{[3
]}

Lemos, Andre P. ^{[3
]}

Michel, Hugo C. C. ^{[3
]}

Braga, Carmela M. P. ^{[3
]}

机构：

[1] Univ Fed Minas Gerais, PPGEE, Belo Horizonte, MG, Brazil

[2] GPEMG CEMIG, Belo Horizonte, MG, Brazil

[3] Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil

来源：

PROCEEDINGS OF THE 2016 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS) | 2016年

关键词：

multivariate statistical process control; principal component analysis; Hotelling's T-2 control chart; thermoelectric power plant; boiler; turbine-generator unit; PIMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Thermoelectric power plants have critical units, such as the boiler and the turbine-generator, which are complex multivariate systems. These units exhibit non-stationary behavior and multiple operational modes that imply constant changes of set points of key performance variables. A methodology based on MSPC (Multivariate Statistical Process Control) techniques and PCA (Principal Component Analysis) is presented with an adaptive mean estimator that deals with frequent changes of set points, both for design and just in time monitoring. The proposed methodology' is implemented in a thermoelectric power plant using a commercial PIMS (Process Information Management System) software suite. Experimental results illustrate and validate the proposition, its just-in-time implementation and usage.

引用

页码：49 / 56

页数：8

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