On fusion of PCA and a physical model-based predictive control strategy for efficient load-cycling operation of a thermal power plant

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9 Citations (Scopus)

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.
LanguageEnglish
Pages231-258
JournalOptimal Control Applications and Methods
Volume28
Issue number4
DOIs
Publication statusPublished - 26 Feb 2007

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Model-based Control
Predictive Control
Cycling
Power Plant
Physical Model
Principal component analysis
Principal Component Analysis
Control Strategy
Power plants
Fusion
Fusion reactions
Model Predictive Control
Sensor
Model predictive control
Heat Exchanger
Sensors
Tubes (components)
Tube
Non-minimum Phase
Thermal Stress

Cite this

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title = "On fusion of PCA and a physical model-based predictive control strategy for efficient load-cycling operation of a thermal power plant",
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.",
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On fusion of PCA and a physical model-based predictive control strategy for efficient load-cycling operation of a thermal power plant. / Prasad, G.

Vol. 28, No. 4, 26.02.2007, p. 231-258.

Research output: Contribution to journalArticle

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