PSO based kernel principal component analysis and multi-class support vector machine for power quality problem classification

dc.contributor.authorPahasa, Jonglak
dc.contributor.authorNgamroo, Issarachai
dc.date.accessioned2026-08-06T10:04:07Z
dc.date.available2026-08-06T10:04:07Z
dc.date.issued2012-03-01
dc.description.abstractElectric power quality (PQ) problems are very important aspects due to the increase in the number of loads which are sensitive to power disturbances. One of the important issues in the PQ problems is to detect and classify disturbance waveforms auto-matically in an efficient approach, because the possible solutions can be determined after the disturbance types are detected. This paper proposes a particle swarm optimization (PSO) based kernel principal component analysis (KPCA) and support vector machine (SVM) for PQ problem classification. Wavelet based multiresolution analysis (MRA) is utilized to extract features for various PQ disturbances. Dimension of these features are then reduced by KPCA so that the noise has less impact on the classification results. The multi-class SVM is used to classify the PQ problem using the dominant KPCA. The PSO is applied to optimize the KPCA and SVM parameters in order to improve the classification performance. The classification process implemented with various PQ events shows that the proposed technique provides more accuracy than the conventional technique under both noisy and noiseless environments. © 2012 ISSN 1349-4198.
dc.identifier.citationInternational Journal of Innovative Computing Information and Control, 8(3 A), 1523-1539, 2012
dc.identifier.issn13494198
dc.identifier.other2-s2.0-84857579104
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4156
dc.sourceInternational Journal of Innovative Computing Information and Control
dc.subjectKernel principal component analysis
dc.subjectMultiresolution analysis
dc.subjectParticle swarm optimization
dc.subjectPower quality classification
dc.subjectSupport vector machine
dc.titlePSO based kernel principal component analysis and multi-class support vector machine for power quality problem classification
dc.typeArticle

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