期刊
OPTIMAL CONTROL APPLICATIONS & METHODS
卷 28, 期 4, 页码 231-258出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/oca.797
关键词
principal component analysis; model predictive control; thermal power plant; load cycling; rate constraints
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-mininium 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 constranits. Copyright (C) 2007 John Wiley & Sons, Ltd.
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