Plant-wide predictive control for a thermal power plant based on a physical plant model

Prasad, Girijesh; Irwin, G.W.; Swidenbank, E.; Hogg, B.W.

doi:10.1049/ip-cta:20000634

Cited by 49 publications

(29 citation statements)

References 5 publications

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“…In Appendix I we derive the full expression for the mean and covariance using this series. Linearization assumes that all second and higher order terms in the Taylor series expansion are negligible, i.e., Taking outer products and expectations, and by exploiting the assumption that the estimation error is approximately zeromean, the mean and covariance are (5) (6) 2 The prediction corresponds to the case when x = ; z = where is the Jacobian of and the fact that has been used. However, the full Taylor series expansion of this function, given in Appendix I, shows that these quantities contain higher order terms that are a function of the statistics of and higher derivatives of the nonlinear transformation.…”

Section: B Propagating Means and Covariances Through Nonlinear Transmentioning

confidence: 99%

“…Both the prediction and update steps of a KF can be written in this form. 2 Taking the multidimensional Taylor series expansion…”

Section: B Propagating Means and Covariances Through Nonlinear Transmentioning

confidence: 99%

“…Estimation in nonlinear systems is extremely important because almost all practical systems-from target tracking [1] to vehicle navigation, from chemical process plant control [2] to dialysis machines-involve nonlinearities of one kind or another. Accurately estimating the state of such systems is extremely important for fault detection and control applications.…”

Section: Introductionmentioning

confidence: 99%

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Unscented Filtering and Nonlinear Estimation

Julier¹,

2004

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Section: B Propagating Means and Covariances Through Nonlinear Transmentioning

confidence: 99%

“…Both the prediction and update steps of a KF can be written in this form. 2 Taking the multidimensional Taylor series expansion…”

Section: B Propagating Means and Covariances Through Nonlinear Transmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unscented Filtering and Nonlinear Estimation

Julier¹,

2004

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“…In recent years, several works have been proposed based on model-predictive control strategies and physical plant models [1][2][3][4][5][6]. The work by [1] is based on a linear plant model and Generalised Predictive Control (GPC) in a combined-cycle power plant.…”

Section: Introductionmentioning

confidence: 99%

“…They claim improved performance with the DPC dealing with cross-couplings, especially in constrained cases. Prasad et al [3][4] adopts an approach of non-linear Model-based Predictive Control (MPC) including stochastic variables for disturbance modelling. The extended Kalman filter is then employed to obtain a predictor for the controller.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Power Plant Master Control

Thomas

2014

Automatika

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In this paper a coordinated master control for a solid fuel power plant has been developed and the performance evaluated in terms of tracking capability, stability and robustness. The control strategy has been model-based predictive control (MPC) and it was evaluated on a nonlinear process model of the Vattenfall power plant Idbäcken in Nyköping, Sweden. The developed master MPC with gain scheduling has better performance compared to the existing PID controller which has been thoroughly studied and tuned in a previous project. The robustness of the proposed master MPC controller against common disturbances and parameter variation has been investigated and it shows that the proposed controller is more robust than the existing PID controller.Key words: Master Predictive Control, Solid fuel power plant, Gain scheduling.Nadre eno modelsko upravljanje elektranom. U ovome radu razvijeno je koordinirano nadre eno upravljanje za elektrane na kruta goriva te su provjereni pokazatelji kvalitete u smislu praćenja, stabilnosti i robustnosti. Koristi se modelsko prediktivno upravljanje koje je provjereno na nelinearnom modelu procesa Vattenfall elektrane Idbäcken u Nyköping, Švedska. Razvijeni nadre eni MPC s prekapčanjem ima bolje performanse u usporedbni s PID regulatorom koji je bio temeljito proučen i podešen u prijašnjem projektu. Istražena je robustnost predloženog nadre enog MPC-a u odnosu na šum i promjene parametara. Pokazuje se da je predloženi regulator robustniji od postojećeg PID regulatora.Ključne riječi: nadre eno prediktivno upravljanje, elektrane na kruta goriva, upravljanje s prekapčanjem

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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

Prasad¹

2007

Optim Control Appl Methods

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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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Plant-wide predictive control for a thermal power plant based on a physical plant model

Cited by 49 publications

References 5 publications

Unscented Filtering and Nonlinear Estimation

Unscented Filtering and Nonlinear Estimation

Model-Based Power Plant Master Control

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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