Publication:
Gas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenance

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dc.contributor.authorYaw C.T.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorLow F.W.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid57883863700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid36130958600en_US
dc.contributor.authorid56513524700en_US
dc.date.accessioned2023-05-29T09:39:52Z
dc.date.available2023-05-29T09:39:52Z
dc.date.issued2022
dc.descriptionElectric power transmission; Gas turbines; Governors; Support vector machines; Abnormal operation; Gas turbine generators; Gas turbine modeling; Generator systems; MATLAB/ SIMULINK; Modeling parameters; Problem recognition; Simulink; Support vectors machine; Transmission systems; Simulinken_US
dc.description.abstractWhen there is a contingency in a major transmission system, it is crucial to locate and detect abnormal parameters using an accurately modeled gas turbine (GT) generator. In this paper, a new method was proposed to model, a GT generator system. Firstly, MATLAB/Simulink was used to rebuild GT-based model which was validated using a model in PSS/E. Secondly, an artificial intelligence (AI) based approach, namely Support Vector Machine (SVM) was used for problem recognition in GT power plant. The results showed that, under steady-state, the electrical power of the rebuilt GT model was the same as the model in PSS/E. The advantage of having the MATLAB / Simulink GT model was the ability to visually observe the output from each block which was not possible in PSS/E. In addition, the average training accuracy was over 90% for the detection of GT governor parameter during abnormal behavior. As an implication, the developed model should be considered to apply in real-world grids for operation engineers to detect abnormal governor parameters in GT in the next stage. In turn, it assists in the restoration of GT to its operating condition and minimizes troubleshooting time. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12004
dc.identifier.doi10.1088/1742-6596/2319/1/012004
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85137699418
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137699418&doi=10.1088%2f1742-6596%2f2319%2f1%2f012004&partnerID=40&md5=83587f0d012833f19e34143b5db3e677
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27123
dc.identifier.volume2319
dc.publisherInstitute of Physicsen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Physics: Conference Series
dc.titleGas Turbine Model Parameter Classification during Abnormal Operation Using Support Vector Machine for Maintenanceen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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