Abstract
Controlling a thermal power plant optimally during load-cycling operation is a very challenging control problem. The control complexity is enhanced further by the possibility of simultaneous occurrence of sensor malfunctions and a plethora of system disturbances. This paper proposes and evaluates the effectiveness of a sensor validation and reconstruction approach using principal component analysis (PCA) in conjunction with a physical plant model. For optimal control under severe operating conditions in the presence of possible sensor malfunctions, a predictive control strategy is devised by appropriate fusion of the PCA-based sensor validation and reconstruction approach and a constrained model predictive control (MPC) technique. As a case study, the control strategy is applied for thermal power plant control in the presence of a single sensor malfunction. In particular, it is applied to investigate the effectiveness and relative advantage of applying rate constraints on main steam temperature and heat-exchanger tube-wall temperature, so that faster load cycling operation is achieved without causing excessive thermal stresses in heat-exchanger tubes. In order to account for unstable and non-minimum phase boiler-turbine dynamics, the MPC technique applied is an infinite horizon non-linear physical model-based state-space MPC strategy, which guarantees asymptotic stability and feasibility in the presence of output and state constraints.
Original language | English |
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Pages (from-to) | 231-258 |
Journal | Optimal Control Applications and Methods |
Volume | 28 |
Issue number | 4 |
DOIs | |
Publication status | Published (in print/issue) - 26 Feb 2007 |
Bibliographical note
Other Details------------------------------------
This paper presents a model predictive control (MPC) strategy, seamlessly combining a sensor validation and reconstruction approach using principal component analysis (PCA) with a physical plant model. To account for unstable and non-minimum phase dynamics, the MPC technique is formulated as an infinite horizon non-linear state space model-based strategy through a mathematical derivation, guaranteeing asymptotic stability and feasibility in the presence of output and state constraints. As a case-study, the strategy is applied for improved thermal power plant control in the presence of a single sensor malfunction. This work extended earlier research undertaken in an EPSRC/industry sponsored project.