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Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

Author

Listed:
  • 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)

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.

Suggested Citation

  • Stefano Dettori & Alessandro Maddaloni & Filippo Galli & Valentina Colla & Federico Bucciarelli & Damaso Checcacci & Annamaria Signorini, 2021. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control," Energies, MDPI, vol. 14(13), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3998-:d:587818
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    References listed on IDEAS

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    3. Eduardo F. Camacho & Antonio J. Gallego & Juan M. Escaño & Adolfo J. Sánchez, 2019. "Hybrid Nonlinear MPC of a Solar Cooling Plant," Energies, MDPI, vol. 12(14), pages 1-22, July.
    4. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
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    1. Andrzej Rusin & Martyna Tomala & Henryk Łukowicz & Grzegorz Nowak & Wojciech Kosman, 2021. "On-Line Control of Stresses in the Power Unit Pressure Elements Taking Account of Variable Heat Transfer Conditions," Energies, MDPI, vol. 14(15), pages 1-21, August.

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