Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

Stefano Dettori, Alessandro Maddaloni, Filippo Galli, Valentina Colla, Federico Bucciarelli, Damaso Checcacci and Annamaria Signorini
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Stefano Dettori: Scuola Superiore Sant’Anna, TeCIP Institute, Information and Communication Technologies for Complex Industrial Systems and Processes (ICT-COISP), Via Moruzzi 1, 56124 Pisa, Italy
Alessandro Maddaloni: Scuola Superiore Sant’Anna, TeCIP Institute, Information and Communication Technologies for Complex Industrial Systems and Processes (ICT-COISP), Via Moruzzi 1, 56124 Pisa, Italy
Filippo Galli: Scuola Superiore Sant’Anna, TeCIP Institute, Information and Communication Technologies for Complex Industrial Systems and Processes (ICT-COISP), Via Moruzzi 1, 56124 Pisa, Italy
Valentina Colla: Scuola Superiore Sant’Anna, TeCIP Institute, Information and Communication Technologies for Complex Industrial Systems and Processes (ICT-COISP), Via Moruzzi 1, 56124 Pisa, Italy
Federico Bucciarelli: Nuovo Pignone Tecnologie s.r.l, Baker Hughes, Via Felice Matteucci 2, 50127 Firenze, Italy
Damaso Checcacci: Nuovo Pignone Tecnologie s.r.l, Baker Hughes, Via Felice Matteucci 2, 50127 Firenze, Italy
Annamaria Signorini: Nuovo Pignone Tecnologie s.r.l, Baker Hughes, Via Felice Matteucci 2, 50127 Firenze, Italy

Energies, 2021, vol. 14, issue 13, 1-30

Abstract: The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

Keywords: steam turbine startup; nonlinear model predictive control; rotor stress control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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