Parameter Selection in a Combined Cycle Power Plant

Niklas Andersson
Lund University, Department of Chemical Engineering, Lund, Sweden

Johan Åkesson
Lund University, Department of Automatic Control, Lund, Sweden/Modelon AB, Lund, Sweden

Kilian Link
Siemens AG, Erlangen, Germany

Stephanie Gallardo Yances
Siemens AG, Erlangen, Germany

Karin Dietl
Siemens AG, Erlangen, Germany

Bernt Nilsson
Lund University, Department of Chemical Engineering, Lund, Sweden

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp14096809

Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:84, s. 809-818

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Publicerad: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


A combined cycle power plant are modelled and considered for calibration. The dynamic model; aimed for start-up optimization; contains 64 candidate parameters for calibration. The number of parameter sets that can be created are huge and an algorithm called subset selection algorithm is used to reduce the number of parameter sets. The algorithm investigates the numerical properties of a calibration from a parameter Jacobean estimated from a simulation of the model with reasonably chosen parameter values. The calibrations were performed with a Levenberg-Marquardt algorithm considering the least squares of eight output signals. The parameter value with the best objective function value resulted in simulations in good compliance to the process dynamics. The subset selection algorithm effectively shows which parameters that are important and which parameters that can be left out.


Combined Cycle Power Plants; Startup; Calibration; Parameter Selection


[1] Kehlhofer, R., Warner, J., Nielsen, H., Backmann, R., Combined-Cycle Gas and Steam Turbine Power Plants. ISBN: 0-87814-736-5, second edition, PennWell Publishing Com-pany, Tulsa, Oklahoma, USA, 1999.

[2] Lind, A., Sällberg, E., Velut, S., Åkesson, J., Gallardo Yances, S., Link, K. Sep. 2012. Start-up Optimization of a Combined Cycle Power Plant, 9th International Modelica Conference. Munich, Germany.

[3] Lind, A., Sällberg, E. Optimization of the Startup Procedure of a Combined Cycle Power Plant, Master’s Thesis, Lund University, Department of Automatic Control, 2012

[4] Casella, F., Pretolani, F. Fast Start-up of a Combined-Cycle Power Plant: A Simulation Study with Modelica. In: Modelica Conference pp. 3–10, Vienna, Austria, 2006.

[5] Casella, F. Leva, A. Modelica open library for power plant simulation: design and experimental validation. In: Proceedings of 3rd International Modelica Conference, pp. 41–50. Linköping, Sweden, 2003.

[6] Casella, F., Farina, M., Righetti, F., Scattolini, R., Faille, D., Davelaar, F., Tica, A., Gueguen, H. Dumur, D. An optimization procedure of the start-up of combined cycle power plants.In: 18th IFAC World Congress, pp. 7043–7048. Milano, Italy, 2011.

[7] Casella, F., Donida, F., Åkesson, J. Objectoriented modeling and optimal control: a case study in power plant start-up. In: 18th IFAC World Congress, pp. 9549–9554. Milano, Italy, 2011.

[8] Shirakawa, M., Nakamoto, M., Hosaka, S. Dynamic simulation and optimization of start-up processes in combined cycle power plants. In: JSME International Journal, vol. 48 (1), pp. 122–128, 2005.

[9] Cintrón-Arias, A., Banks, H. T., Capaldi, A., Lloyd, A. L., 2009. A Sensitivity Matrix Based Methodology for Inverse Problem Formulation. Journal of Inverse and Ill-Posed Problems, 17(6), 545-564.

[10] Andersson N., Larsson, P.-O., Åkesson, J., Carlsson, N., Skålén, S. Nilsson, B. Parameter selection in the parameter estimation of grade transitions in a polyethylene plant. submitted for publication.

[11] Edgar, R., Himmelblau, D., 1988. Optimization of Chemical Processes, 1st Edition. McGraw-Hill, New York, NY.

[12] Englezos, P., Kalogerakis, N., 2000. Applied parameter estimation for chemical engineers, 1st Edition. CRC Press.

[13] Storn, K., Price, R., Lampinen, J. 2005 Differential Evolution – A Practical Approach to Global Optimization, Springer-Verlag, Berlin.

[14] Comparison of gradient methods for the solution of nonlinear parameter estimation problems. SIAM Journal on Numerical Analysis 7 (1), 157-186. URL http://www.jstor.org/stable/2949590

[15] Vassiliadis, V., 1993. Computational solution of dynamic optimization problem with general differential-algebraic constraints. Ph.D. thesis, Imperial Collage, London, UK

[16] The Modelica Association, 2011. The Modelica Association Home Page. http://www.modelica.org

[17] Åkesson, J., and Årzén, K.-E., Gäfvert, M., Bergdahl, T., Tummescheit, H., nov 2010. Modeling and Optimization with Optimica and JModelica.org—Languages and Tools for solving large-scale dynamic optimization problem. Computers and Chemical Engineering 34 (11), 1737–1749.

[18] Wächter, A., Biegler, L. T., 2006. On the implementation of an interior-point filter linesearch algorithm for large-scale nonlinear programming. Mathematical Programming 106 (1), 25–58

[19] Andersson, C., Andreasson, J., Führer, C., Å kesson, J., 2012. A workbench for multibody systems ode and dae solvers. In: 2nd Joint International Conference on Multibody System Dynamics. Stuttgart, Germany.

[20] Hindmarsh, A. C., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., Woodward, C. S., September 2005. Sundials: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans. Math. Softw. 31, 363–396 URL http://dx.doi.org/10.1145/1089014.1089020

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