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: Contribution to journalArticle

79 Citations (Scopus)

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.
LanguageEnglish
Pages176-182
JournalIEEE Transactions on Energy Conversion
Volume13
Issue number2
Publication statusPublished - Jun 1998

Fingerprint

Power plants
Neural networks
Steam
Oil fired boilers
Controllers
MIMO systems
Dynamic models
Temperature
Hot Temperature

Cite this

@article{e641316d3a6149449541c224fbb8aa42,
title = "A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied in Thermal Power Plant Control",
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.",
author = "G Prasad and E Swidenbank and Hogg, {B W}",
year = "1998",
month = "6",
language = "English",
volume = "13",
pages = "176--182",
number = "2",

}

A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied in Thermal Power Plant Control. / Prasad, G; Swidenbank, E; Hogg, B W.

Vol. 13, No. 2, 06.1998, p. 176-182.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied in Thermal Power Plant Control

AU - Prasad, G

AU - Swidenbank, E

AU - Hogg, B W

PY - 1998/6

Y1 - 1998/6

N2 - 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.

AB - 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.

M3 - Article

VL - 13

SP - 176

EP - 182

IS - 2

ER -