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doctoral thesis
Model predictive control in hydro turbine power control
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:953422
Beus, Mateo
University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Energy and Power Systems

Cite this document

Beus, M. (2021). Model predictive control in hydro turbine power control (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:953422

Beus, Mateo. "Model predictive control in hydro turbine power control." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:953422

Beus, Mateo. "Model predictive control in hydro turbine power control." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:953422

Beus, M. (2021). 'Model predictive control in hydro turbine power control', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 September 2023, https://urn.nsk.hr/urn:nbn:hr:168:953422

Beus M. Model predictive control in hydro turbine power control [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2023 September 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:953422

M. Beus, "Model predictive control in hydro turbine power control", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:953422

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Metadata
TitleModel predictive control in hydro turbine power control
Title (croatian)Modelsko prediktivno upravljanje u regulaciji snage hidroturbine
AuthorMateo Beus
MentorHrvoje Pandžić (mentor)
Committee memberHrvoje Pandžić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Energy and Power Systems)
Zagreb
Defense date and country2021, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
Hydro turbine governor applications mainly rely on classical Proportional-Integral-Derivative (PID) controllers. The main weakness of classical controllers is their design based on a linear plant model and a fixed parameter scheme. This means that a classical controller can perform optimally only at the operating point chosen during the controller design. However, hydro power plants are highly nonlinear systems, thus alternative control approaches based on adaptive parameters are needed. This research investigates the possibility of applying model predictive controller (MPC) as the load/frequency controller in hydro turbine governors. In the first part of the research the MPC algorithm is validated on the nonlinear simulation model of a laboratory hydro power plant, while in the second part of the research the MPC controller is implemented on a programmable logic controller that is used as a hydro turbine governor in the hydro power plant available in the Smart Grid laboratory at the University of Zagreb Faculty of Electrical Engineering and Computing. Furthermore, MPC controller behavior and practical implementation potential are validated for different operating conditions. Scientific contribution of the thesis consists of the following: • Model predictive control algorithm for a load/frequency controller of a hydro turbine governor • Methodology for real-time implementation of model predictive control algorithm using programmable logic controller, as well as an evaluation of MPC algorithm’s practical implementation potential for a load/frequency controller of a hydro turbine governor
Abstract (croatian)
Turbinski regulatori u hidroelektranama uglavnom se temelje na primjeni klasičnih proporcionalno-integracijsko-derivacijskih regulatora. Glavni nedostatak klasičnih regulatora je njihov dizajn baziran na linearnom modelu postrojenja te upotrebi fiksnih parametara regulatora. To znači da klasični regulatori mogu osigurati optimalno ponašanje sustava samo u okolini radne točke za koju su projektirani. Budući da su hidroelektrane izrazito nelinearni sustavi, potrebno je razmotriti alternativne upravljačke pristupe bazirane na primjeni adaptivnih parametara regulatora. Ovo istraživanje istražuje mogućnost primjene modelskog prediktivnog regulatora (MPC) kao regulatora kao regulatora snage unutar turbinske regulacije hidroelektrane. U prvom dijelu istraživanja, MPC algoritam je validiran na nelinearnom simulacijskom modelu laboratorijske hidroelektrane, dok je u drugom dijelu istraživanja MPC algoritam implementiran na programibilni logički kontroler koji se koristi kao turbinski regulator laboratorijske hidroelektrane dostupne u sklopu Laboratorija za napredne elektroenergetske mreže pri Fakultetu elektrotehnike i računarstva Sveučilišta u Zagrebu, te se validiralo njegovo ponašanje i potencijal praktične primjene u različitim pogonskim stanjima. Znanstveni doprinos disertacije sastoji se od sljedećeg: - Algoritam modelskog prediktivnog regulatora snage u sklopu turbinskog regulatora hidroelektrane - Metodologija izvedbe u stvarnom vremenu algoritma modelskog prediktivnog upravljanja koristeći programibilni logički kontroler te evaluacija njegovog potencijala za praktičnu primjenu u sklopu turbinskog regulatora hidroelektrane
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:953422
Study programmeTitle: Doctoral study programme "Electrical Engineering and Computing"
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, po-dručje tehničkih znanosti (doktor/doktorica znanosti, po-dručje tehničkih znanosti)
Catalog URLhttp://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=52610
Type of resourceText
Extentix, 84 str.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
RepositoryFER Repository
Created on2022-04-27 11:34:04

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