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A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Prasad, G, Irwin, GW, Swidenbank, E and Hogg, BW (2002) A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection. Transactions of Institute of Measurement and Control, 24 (2). pp. 107-128. [Journal article]

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Abstract

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523–37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-Žred power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat ow on the boiler-side, while condenser vacuumwas uctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:8003
Deposited By:Professor Girijesh Prasad
Deposited On:01 Feb 2010 12:14
Last Modified:01 Feb 2010 12:14

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