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A model predictive control approach to generator maintenance scheduling

Dissertation (MSc)--University of Pretoria, 2011.

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Other Authors: Zhang, Jiangfeng
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Published: University of Pretoria 2013
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access_status_str Open Access
author2 Zhang, Jiangfeng
author_browse Zhang, Jiangfeng
author_facet Zhang, Jiangfeng
collection Thesis
dc_rights_str_mv © 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc)--University of Pretoria, 2011.
format Thesis
id oai:repository.up.ac.za:2263/28126
institution University of Pretoria (South Africa)
last_indexed 2026-06-10T12:40:08.409Z
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provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2013
publishDateRange 2013
publishDateSort 2013
publisher University of Pretoria
publisherStr University of Pretoria
record_format dspace
source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/28126 A model predictive control approach to generator maintenance scheduling Zhang, Jiangfeng Xia, Xiaohua Ekpenyong, Uduakobong Edet Kontrole Partikel swerm optimalisering Optimalisering Onderhoud Modellering Kragopwekker Kragopwekker onderhoudskedule Control Swarm intelligence Model predictive control Genetic algorithm Swerm intelligensie Model voorspellende kontrole Genetiese algoritme Generator maintenance scheduling Generators Modelling Maintenance Optimization Particle swarm optimization (PSO) UCTD Dissertation (MSc)--University of Pretoria, 2011. The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. Electrical, Electronic and Computer Engineering Unrestricted 2013-09-07T12:55:26Z 2011-10-10 2013-09-07T12:55:26Z 2011-09-06 2011-10-10 2011-09-22 Dissertation Ekpenyong, UE 2011, A model predictive control approach to generator maintenance scheduling, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/28126 > E11/9/132/gm http://hdl.handle.net/2263/28126 http://upetd.up.ac.za/thesis/available/etd-09222011-095851/ © 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle Kontrole
Partikel swerm optimalisering
Optimalisering
Onderhoud
Modellering
Kragopwekker
Kragopwekker onderhoudskedule
Control
Swarm intelligence
Model predictive control
Genetic algorithm
Swerm intelligensie
Model voorspellende kontrole
Genetiese algoritme
Generator maintenance scheduling
Generators
Modelling
Maintenance
Optimization
Particle swarm optimization (PSO)
UCTD
A model predictive control approach to generator maintenance scheduling
title A model predictive control approach to generator maintenance scheduling
title_full A model predictive control approach to generator maintenance scheduling
title_fullStr A model predictive control approach to generator maintenance scheduling
title_full_unstemmed A model predictive control approach to generator maintenance scheduling
title_short A model predictive control approach to generator maintenance scheduling
title_sort model predictive control approach to generator maintenance scheduling
topic Kontrole
Partikel swerm optimalisering
Optimalisering
Onderhoud
Modellering
Kragopwekker
Kragopwekker onderhoudskedule
Control
Swarm intelligence
Model predictive control
Genetic algorithm
Swerm intelligensie
Model voorspellende kontrole
Genetiese algoritme
Generator maintenance scheduling
Generators
Modelling
Maintenance
Optimization
Particle swarm optimization (PSO)
UCTD
url http://hdl.handle.net/2263/28126
http://upetd.up.ac.za/thesis/available/etd-09222011-095851/