Statistical model for power plant performance monitoring and analysis

被引：7

作者：

Pan, Li ^{[1
]}

Flynn, Damian ^{[1
]}

Cregan, Michael ^{[1
]}

机构：

[1] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland

来源：

2007 42ND INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE, VOLS 1-3 | 2007年

关键词：

power plant modelling; performance analysis; partial least squares; radial basis function; genetic algorithm;

D O I：

10.1109/UPEC.2007.4468931

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A novel approach for monitoring and analysing power plant operation and performance is presented utilizing statistical modelling technology, specifically linear partial least squares (PI-S) and non-linear radial basis function (RBF-PLS) models. For the RBF neural network, a genetic algorithm (GA) is employed to optimise the model parameters. The potential of these models for signal and error prediction, and performance analysis is demonstrated utilizing, data from a combined cycle gas turbine (CCGT).

引用

页码：121 / 126

页数：6

共 50 条

[1] Power Plant Performance Monitoring Using Statistical Methodology Approach
Swirski, Konrad
[J]. JOURNAL OF POWER TECHNOLOGIES, 2011, 91 (02): : 63 - 76
[2] Analysis and monitoring of the combustion performance in a biomass power plant
Silva, Joao Pedro
Teixeira, Senhorinha
Grilo, Elson
Peters, Bernhard
Teixeira, Jose Carlos
[J]. CLEANER ENGINEERING AND TECHNOLOGY, 2021, 5
[3] Statistical analysis and optimum performance of the gas turbine power plant
Ibrahim, Thamir K.
Rahman, M. M.
Mohammed, M. K.
Basrawi, Firdaus
[J]. INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING, 2016, 13 (01) : 3215 - 3225
[4] ANALYSIS OF POWER PLANT DYNAMICS BY A STATISTICAL METHOD
NAKAMURA, H
IMAIZUMI, H
[J]. ELECTRICAL ENGINEERING IN JAPAN, 1967, 87 (02) : 44 - &
[5] Application of a variance analysis for the performance monitoring systems of power plant gas turbines
Turbomachine Research Institute, Shanghai Jiaotong University, Shanghai 200030, China
不详
[J]. Reneng Dongli Gongcheng, 2007, 2 (134-137+141):
[6] Performance monitoring and diagnosis of multivariable model predictive control using statistical analysis
Zhang, Q
Li, SY
[J]. CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2006, 14 (02) : 207 - 215
[7] Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis
张强
李少远
[J]. Chinese Journal of Chemical Engineering, 2006, (02) : 207 - 215
[8] Monitoring of a Thermoelectric Power Plant based on Multivariate Statistical Process Control
Fonseca, Joyce M. F.
Sousa, Bruno M.
Aguiar, Webber E.
Braga, Anisio R.
Lemos, Andre P.
Michel, Hugo C. C.
Braga, Carmela M. P.
[J]. PROCEEDINGS OF THE 2016 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS), 2016, : 49 - 56
[9] PERFORMANCE MONITORING ENHANCES POWER-PLANT OPERATIONS
SMITH, DJ
[J]. POWER ENGINEERING, 1988, 92 (08) : 20 - 25
[10] ONLINE MONITORING ENERGIZES POWER-PLANT PERFORMANCE
BOOTH, K
[J]. INTECH, 1991, 38 (10) : 44 - 46

← 1 2 3 4 5 →