A Multivariable Controller For Economical Fossil Power Plant Operation

G Prasad, E Swidenbank, B W Hogg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To realize the most economical operation of the plant, requires the controller recognize the interaction between multiple inputs and the constraints imposed by the physical limits of the system. A model-based multivariable predictive controller with real-time constrained optimization has been designed and implemented. Artificial neural networks (ANN) modeling technique has been used for identifying global dynamic models of the plant variables. Results are given to demonstrate the superiority of the controller in controlling the main steam temperature and reheat steam temperature during rapid load changes.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages832-835
Number of pages4
Publication statusPublished - Sep 1996
EventUniversities Power Engineering Conference 1996 - Iraklio, Greece.
Duration: 1 Sep 1996 → …

Conference

ConferenceUniversities Power Engineering Conference 1996
Period1/09/96 → …

Fingerprint

Power plants
Controllers
Steam
Constrained optimization
Dynamic models
Neural networks
Temperature

Cite this

Prasad, G., Swidenbank, E., & Hogg, B. W. (1996). A Multivariable Controller For Economical Fossil Power Plant Operation. In Unknown Host Publication (pp. 832-835)
Prasad, G ; Swidenbank, E ; Hogg, B W. / A Multivariable Controller For Economical Fossil Power Plant Operation. Unknown Host Publication. 1996. pp. 832-835
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Prasad, G, Swidenbank, E & Hogg, BW 1996, A Multivariable Controller For Economical Fossil Power Plant Operation. in Unknown Host Publication. pp. 832-835, Universities Power Engineering Conference 1996, 1/09/96.

A Multivariable Controller For Economical Fossil Power Plant Operation. / Prasad, G; Swidenbank, E; Hogg, B W.

Unknown Host Publication. 1996. p. 832-835.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - To realize the most economical operation of the plant, requires the controller recognize the interaction between multiple inputs and the constraints imposed by the physical limits of the system. A model-based multivariable predictive controller with real-time constrained optimization has been designed and implemented. Artificial neural networks (ANN) modeling technique has been used for identifying global dynamic models of the plant variables. Results are given to demonstrate the superiority of the controller in controlling the main steam temperature and reheat steam temperature during rapid load changes.

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Prasad G, Swidenbank E, Hogg BW. A Multivariable Controller For Economical Fossil Power Plant Operation. In Unknown Host Publication. 1996. p. 832-835