Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163092
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dc.contributor.authorVinayagam, Arangarajanen_US
dc.contributor.authorMohammad Lutfi Othmanen_US
dc.contributor.authorVeerasamy, Veerapandiyanen_US
dc.contributor.authorBalaji, Suganthi Saravanen_US
dc.contributor.authorRamaiyan, Kalaivanien_US
dc.contributor.authorRadhakrishnan, Padmavathien_US
dc.contributor.authorRaman, Mohan Dasen_US
dc.contributor.authorNoor Izzri Abdul Wahaben_US
dc.date.accessioned2022-11-21T05:29:56Z-
dc.date.available2022-11-21T05:29:56Z-
dc.date.issued2022-
dc.identifier.citationVinayagam, A., Mohammad Lutfi Othman, Veerasamy, V., Balaji, S. S., Ramaiyan, K., Radhakrishnan, P., Raman, M. D. & Noor Izzri Abdul Wahab (2022). A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network. PloS One, 17(1), e0262570-. https://dx.doi.org/10.1371/journal.pone.0262570en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttps://hdl.handle.net/10356/163092-
dc.description.abstractThis study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.en_US
dc.language.isoenen_US
dc.relation.ispartofPloS oneen_US
dc.rights© 2022 Vinayagam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power networken_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1371/journal.pone.0262570-
dc.description.versionPublished versionen_US
dc.identifier.pmid35085307-
dc.identifier.scopus2-s2.0-85123708581-
dc.identifier.issue1en_US
dc.identifier.volume17en_US
dc.identifier.spagee0262570en_US
dc.subject.keywordsDiscrete Wavelet Transformen_US
dc.subject.keywordsWavelet Analysisen_US
dc.description.acknowledgementThis work was supported by the Geran Putra Berimpak from the Universiti Putra Malaysia (GPB UPM) under Grant UPM/800-3/3/1/GPB/ 2019/9671700. The grant was received by Dr Mohammad Lutfi Othman.en_US
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