A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control

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 nonlinear long-range predictive control algorithm is derived, which provides offset-free closed-loop behavior with a proper a...

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Bibliographic Details
Published in:IEEE transactions on energy conversion 1998-06, Vol.13 (2), p.176-182
Main Authors: Prasad, G., Swidenbank, E., Hogg, B.W.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Boilers
Computer simulation
Energy
Energy. Thermal use of fuels
Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc
Error correction
Exact sciences and technology
Installations for energy generation and conversion: thermal and electrical energy
Installations for industrial steam generation
Neural networks
Oil fired boilers
Power generation
Power system modeling
Prediction algorithms
Predictive control
Predictive control systems
Predictive models
Pressure control
Temperature control
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Summary: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 nonlinear 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 into 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.
ISSN:0885-8969
1558-0059
DOI:10.1109/60.678982