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Steam turbine rotor stress control through nonlinear model predictive control

Title: Steam turbine rotor stress control through nonlinear model predictive control
Authors: Dettori S.; Maddaloni A.; Galli F.; Colla V.; Bucciarelli F.; Checcacci D.; Signorini A.
Contributors: Dettori, S.; Maddaloni, A.; Galli, F.; Colla, V.; Bucciarelli, F.; Checcacci, D.; Signorini, A.
Publication Year: 2021
Collection: Scuola Universitaria Superiore Pisa Sant'Anna: CINECA IRIS
Subject Terms: Nonlinear model predictive control; Rotor stress control; Steam turbine startup
Description: 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.
Document Type: article in journal/newspaper
Language: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000671165600001; volume:14; issue:13; firstpage:3998; numberofpages:30; journal:ENERGIES; http://hdl.handle.net/11382/539030
DOI: 10.3390/en14133998
Availability: http://hdl.handle.net/11382/539030; https://doi.org/10.3390/en14133998
Accession Number: edsbas.8D37D598
Database: BASE