energies
Article
Steam Turbine Rotor Stress Control through Nonlinear Model
Predictive Control
Stefano Dettori
1,
* , Alessandro Maddaloni
1
, Filippo Galli
1
, Valentina Colla
1,
* , Federico Bucciarelli
2
,
Damaso Checcacci
2
and Annamaria Signorini
2
Citation: Dettori, S.; Maddaloni, A.;
Galli,F.; Colla, V.; Bucciarelli, F.;
Checcacci, D.; Signorini, A. Steam
Turbine Rotor Stress Control through
Nonlinear Model Predictive Control.
Energies 2021, 14, 3998. https://
doi.org/10.3390/en14133998
Academic Editors: José María
Maestre, Carlos Bordons and Juan
Manuel Escaño
Received: 12 May 2021
Accepted: 28 June 2021
Published: 2 July 2021
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1
Scuola Superiore Sant’Anna, TeCIP Institute, Information and Communication Technologies for Complex
Industrial Systems and Processes (ICT-COISP), Via Moruzzi 1, 56124 Pisa, Italy;
a.maddaloni@santannapisa.it (A.M.); filippo.galli@santannapisa.it (F.G.)
2
Nuovo Pignone Tecnologie s.r.l, Baker Hughes, Via Felice Matteucci 2, 50127 Firenze, Italy;
federico.bucciarelli@bakerhughes.com (F.B.); damaso.checcacci@bakerhughes.com (D.C.);
annamaria.signorini@bakerhughes.com (A.S.)
* Correspondence: s.dettori@santannapisa.it (S.D.); valentina.colla@santannapisa.it (V.C.)
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
1. Introduction
In the current energy market, renewable sources and flexible power plants are increas-
ingly exploited in order to meet the energy demand of increasingly connected cities [1],
where smart grids are going to become a consolidated reality [2,3]. In this context, the
design of new power plants must aim at increasing the level of flexibility, with a resulting
multitude of challenges in terms of degradation of the components, restrictions related
to the environmental impact, and required level of workforce specialization. For these
reasons, the design of Steam Turbines (STs) nowadays must take into account frequent dis-
continuous operations. For instance, in the case of Concentrated Solar Power Plants (CSPP),
the inlet steam highly depends on the weather conditions [4], while in industrial power
generation, the discontinuity is related to the scheduling of the electricity production, opti-
mized according to the prices of energy media (natural gas and electricity) that vary on an
hourly or daily basis. One of the most important aspects related to discontinuous operation
is the need to enforce protection against stress induced by thermal transients, which, due to
the continuous changes in the steam conditions, can lead to faster aging of the turbine and,
in extreme cases, to component failure due to nucleation and propagation of cracks. While
almost every ST component is affected by thermal stress aging, transient thermal stresses in
rotors often represent the factor determining the minimum allowable start-up time and the
maximum allowable production during start-up. The start-up optimization is carried out
Energies 2021, 14, 3998. https://doi.org/10.3390/en14133998 https://www.mdpi.com/journal/energies