Publication:
Classification of the harmonic load types using Multi-Layer Extreme Learning Machine

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dc.citedby3
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorLi X.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid57775484100en_US
dc.date.accessioned2023-05-29T06:54:01Z
dc.date.available2023-05-29T06:54:01Z
dc.date.issued2018
dc.descriptionAggregates; Classification (of information); Electric power systems; Harmonic analysis; Knowledge acquisition; Learning systems; Auto encoders; Classification results; Electricity consumers; Extreme learning machine; Harmonic loads; Load characteristics; Power system; Power system applications; Electric power system planningen_US
dc.description.abstractThis paper presents a neural network approach intended to aid electricity consumers or power system planner in the task of classification of harmonic load types using the measurements (data samples) collected from one of the power station in Malaysia. In order to allow the classification of the type of harmonic loads, harmonic currents order produced by aggregate harmonic loads and the level of emission are modelled using the Multi-Layer Extreme Learning Machine with autoencoder (hereinafter denoted as ML-ELM-AE). The feasibility of ML-ELM-AE on the classification of the Harmonic empirical data set will be probed, for when the classification results of the Harmonic load types become available, it can come in handy to power system analyst or engineers for analysis in later stage. They can use it to determine the harmonic distortion patterns and to characterize the harmonic currents at network buses. Depending on whether the aggregate load is residential, commercial, or industrial, the load characteristics in terms of its harmonic contents are likely to be different. The achieved results demonstrate the effectiveness of the investigated technique in dealing with the real world power system application of harmonic load type classification, that can be useful in providing good indication to the power system or distribution network planner. � 2018 Institution of Engineering and Technology. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.issueCP749
dc.identifier.scopus2-s2.0-85061347432
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061347432&partnerID=40&md5=9b9447c7da3f6b61cbd86701960629b4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23994
dc.identifier.volume2018
dc.publisherInstitution of Engineering and Technologyen_US
dc.sourceScopus
dc.sourcetitleIET Conference Publications
dc.titleClassification of the harmonic load types using Multi-Layer Extreme Learning Machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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