Prospects of model predictive control of the drum level at a 225 MW combined cycle power plant

Nieto-Chaupis, Huber

doi:10.1109/etcm.2016.7750860

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In [16], a state-space MPC method is applied to the control of the core power in a pressurised water reactor. Additional applications of MPC to thermal power plant processes can be found in [36][37][38] for coal-fired power plants, [19,20] for gas-fired power plants, and in [17] for nuclear power plants.…”

Section: Review Of Mpc Applications In Power Plantsmentioning

confidence: 99%

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Beus

Pandžić

2020

IET Renewable Power Generation

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Traditionally, hydro turbine governor applications mainly rely on classical proportional-integral-derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm.

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Section: Review Of Mpc Applications In Power Plantsmentioning

confidence: 99%

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Beus

Pandžić

2020

IET Renewable Power Generation

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“…2 So the dip found on data can be interpreted as the apparition of two sharp peaks as consequence of variable interaction. In this case the drum pressure and valve defines a type of interaction inside the drum [5]. It leads to modify roughly the morphology of the drum level to some extent.…”

Section: Testing the Model Through Numerical Integrationmentioning

confidence: 99%

“…From matrix MIMO system defined in (5) it is easy to see that the interactions are now governed by the product V…”

Section: B Introduction To Variables Interactionmentioning

confidence: 99%

Understanding Dynamics of Combined Cycles in Energy Plants with Theories of Variables Interaction: A pedagogical Approach to the Undergraduate Level of Engineering

Nieto-Chaupis¹

2018

Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Innovation in Educat

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In this note we present a pedagogical approach of variable interactions applied to a 2×2 MIMO system and its confrontation to acquired data from a combined cycle power plant, is studied. The Laplace space is used as a scenario to compute basic operations and subsequently the resulting relations are transformed in convolution integrals. The term responsible of interaction is then simulated and confronted to acquired data from dynamics variable of the power plant. Concretely, attention is paid to the drum level which turns out to be the variable of most importance inside the combined cycle power plant. Our approach is as a pedagogical introduction with a theoretical model sustained even for those cases where the temporal evolution of the drum level is notably affected by intrinsic fluctuations. Thus, a discrepancy between model and data results to be of order of 5%. It would suggest that the interaction variable formalism might be of interest to design model-based control theories which could be applied to those MIMO systems which are plagued of intrinsic and stochastic fluctuations.

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Integrated optimization of process control and its effect on structural integrity – A systematic review

Szentannai

Fekete

2022

Engineering Failure Analysis

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Prospects of model predictive control of the drum level at a 225 MW combined cycle power plant

Cited by 3 publications

References 8 publications

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Application of an adaptive model predictive control algorithm on the Pelton turbine governor control

Understanding Dynamics of Combined Cycles in Energy Plants with Theories of Variables Interaction: A pedagogical Approach to the Undergraduate Level of Engineering

Integrated optimization of process control and its effect on structural integrity – A systematic review

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