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MOSELM approach for Voltage Stability Indicator using phasor measurement units

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dc.citedby1
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorSaadun N.en_US
dc.contributor.authorAbdullah S.K.S.en_US
dc.contributor.authorMohd Sarmin M.K.N.en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55612145600en_US
dc.contributor.authorid56926947000en_US
dc.contributor.authorid54412554100en_US
dc.date.accessioned2023-12-28T06:30:17Z
dc.date.available2023-12-28T06:30:17Z
dc.date.issued2012
dc.description.abstractVoltage stability assessment is important in order to ensure a stable power system. Two algorithms were discussed in this paper which looks into estimating voltage stability based upon Thevenin Equivalent values in a system using Voltage and Current Phasors for different loading values. The first algorithm uses a Kalman filter based formulation. The second method uses an Online Learning approach known as the Modified Online Sequence Extreme Learning Machine (MOSELM). Results show that the Kalman Filter approach is capable of analyzing voltage stability but it requires some user specified information for tuning. On the other hand, the MOSELM approach show that it is capable of producing the same result as the Kalman Filter approach but require less amount of user specified information. � 2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6450267
dc.identifier.doi10.1109/PECon.2012.6450267
dc.identifier.epage514
dc.identifier.scopus2-s2.0-84874499995
dc.identifier.spage510
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84874499995&doi=10.1109%2fPECon.2012.6450267&partnerID=40&md5=9cc9a915e587dcc8c3f13ebf663d629b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29510
dc.pagecount4
dc.sourceScopus
dc.sourcetitlePECon 2012 - 2012 IEEE International Conference on Power and Energy
dc.subjectArtificial Intelligence
dc.subjectKalman Filter
dc.subjectOnline Sequential Extreme Learning Machine
dc.subjectPhasor Measurement Units
dc.subjectVoltage Stability
dc.subjectAlgorithms
dc.subjectArtificial intelligence
dc.subjectElectric power system interconnection
dc.subjectKalman filters
dc.subjectKnowledge acquisition
dc.subjectLearning systems
dc.subjectLoading
dc.subjectPhasor measurement units
dc.subjectVoltage control
dc.subjectCurrent phasors
dc.subjectExtreme learning machine
dc.subjectFilter approach
dc.subjectLoading values
dc.subjectOnline learning
dc.subjectOnline sequential extreme learning machine
dc.subjectThevenin equivalent
dc.subjectVoltage stability assessment
dc.subjectVoltage stability indicators
dc.subjectVoltage stabilizing circuits
dc.titleMOSELM approach for Voltage Stability Indicator using phasor measurement unitsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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