A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied in Thermal Power Plant Control

G Prasad, E Swidenbank, B W Hogg

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

Abstract

A constrained multivariable control strategy along with its application in more efficient thermal power plant control is presented in this paper. A neural network model-based non-linear long-range predictive control algorithm is derived, which provides offset-free closed-loop behavior with a proper and consistent treatment of modeling errors and other disturbances. A multivariable controller is designed and implemented using this algorithm. The system constraints are taken in to account by including them in the control algorithm using real-time optimization. By running a simulation of a 200 MW oil-fired drum-boiler thermal power plant over a load-profile along with suitable PRBS signals superimposed on controls, the operating data is generated. Neural network (NN) modeling techniques have been used for identifying global dynamic models (NNARX models) of the plant variables off-line from the data. To demonstrate the superiority of the strategy in a MIMO case, the controller has been used in the simulation to control main steam pressure and temperature, and reheat steam temperature during load-cycling and other severe plant operating conditions.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE Power and Energy Society
Number of pages7
Publication statusPublished - Jul 1997
EventIEEE Power Engineering Society Summer Meeting, 20-24 July, Berlin, Germany - Berlin, Germany.
Duration: 1 Jul 1997 → …

Conference

ConferenceIEEE Power Engineering Society Summer Meeting, 20-24 July, Berlin, Germany
Period1/07/97 → …

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