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Adaptive Fuzzy Sugeno Large of Maxima Optimization for Gas Turbine Biofuel Speed Controllers

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dc.contributor.authorAli M.en_US
dc.contributor.authorSimic M.en_US
dc.contributor.authorAlrahman A.en_US
dc.contributor.authorNagi F.en_US
dc.contributor.authorid57191335054en_US
dc.contributor.authorid35215976200en_US
dc.contributor.authorid57191330197en_US
dc.contributor.authorid56272534200en_US
dc.date.accessioned2023-05-29T06:13:34Z
dc.date.available2023-05-29T06:13:34Z
dc.date.issued2016
dc.descriptionBiofuels; Combustion; Controllers; Fossil fuel deposits; Fossil fuels; Fuels; Gases; Knowledge based systems; Ship propulsion; Speed; Speed control; Alternative to fossil fuels; Electrical power generation; Fossil fuel resources; Fuzzy controllers; Fuzzy Sugeno Large of Maxima; Gas turbine modeling; Renewable energies; Speed controller; Gas turbinesen_US
dc.description.abstractModern day gas turbines are important sources of electrical power generation, and are regarded as the prime movers in aerospace, marine propulsion. The depletion of fossil fuel resources has paved the way for the usage of biofuel and renewable energy. The challenge to this is the fact that biofuel has been considered as an alternative to fossil fuel for power generation to ensure the reliability and dependability of renewable fuels in a complex multi-domain systems such as gas turbines. The main objective of this study is to optimize the fuel control system parameters of the Rowen gas turbine model and the turbine speed controllers. Furthermore, to render the Rowen model simulation relevant for biofuel applications, real-time experimental data on speed and loads were obtained. Thus, this paper proposes micro turbines that run on biofuel-based tuned Fuzzy Sugeno Large of Maxima (FSLOM). Four controllers were used to track the speed and load data. The experimental results show that, fuzzy controllers with online gain tuning, result in the best speed controllers performance (2.0- 1.05 Sec) for the biofuel operation compared to other controllers. � 2016 The Authors. Published by Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.procs.2016.08.197
dc.identifier.epage1517
dc.identifier.scopus2-s2.0-84988874432
dc.identifier.spage1507
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84988874432&doi=10.1016%2fj.procs.2016.08.197&partnerID=40&md5=83a377d05e29b478c55d3af4b896b0d9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22944
dc.identifier.volume96
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleProcedia Computer Science
dc.titleAdaptive Fuzzy Sugeno Large of Maxima Optimization for Gas Turbine Biofuel Speed Controllersen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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