Prasad, G, Irwin, GW, Swidenbank, E and Hogg, BW (1999) PLANT-WIDE MODEL PREDICTIVE CONTROL OF A THERMAL POWER PLANT. In: IASTED International Conference, Control and Applications (CA'99), 25-30 July, Banff, Canada, Banff, Canada. IASTED International Conference, Control and Applications (CA'99). 6 pp. [Conference contribution]
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This paper reports a non-linear model predictive control (NPMPC) strategy for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and extended Kalman filtering (EKF) to obtain a linear state-space model. The linear model and a quadratic programming routine are then used to design a constrained long-range predictive control routine. A special feature of the control strategy is the selection of a specific set of model parameters for on-line closed-loop estimation to account for time-varying system characteristics resulting from major system disturbances and ageing. Acting as stochastic disturbance states, these parameters provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs and system states. A plant model with 14 non-linear ODEs, simulating the dominant characteristics of a 200 MW oil-fired power plant at Ballylumford, N. Ireland, has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results during large system disturbances and extremely high rate of load changes right across the operating range. The results compare favourably to those obtained with non-linear state-space GPC method designed under similar conditions.
|Item Type:||Conference contribution (Paper)|
|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 > Intelligent Systems Research Centre|
Computer Science Research Institute
|Deposited By:||Professor Girijesh Prasad|
|Deposited On:||16 May 2011 10:55|
|Last Modified:||09 Dec 2015 10:57|
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