PSO-based learning of support vector machines for adaptive TCSC

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dc.contributor.authorPahasa, Jonglak
dc.contributor.authorHongesombut, Komsan
dc.contributor.authorNgamroo, Issarachai
dc.date.accessioned2026-08-06T10:04:23Z
dc.date.available2026-08-06T10:04:23Z
dc.date.issued2012-06-12
dc.description.abstractThis paper proposes the design of an adaptive thyristor controlled series capacitor (TCSC) using support vector machines (SVMs) and particle swarm optimization (PSO). The SVMs for an adaptive TCSC are trained by the data obtained from a multi-machine power system. PSO is used to optimize the SVM parameters based on k-fold cross-validation technique. The TCSC parameters produced by SVMs can be adapted by various operating conditions. Simulation results in a two-area four-machine power system demonstrate that the proposed SVMs for an adaptive TCSC is much superior to the conventional TCSC with fixed parameters under various operating conditions.
dc.identifier.citationProceedings of the IASTED Asian Conference on Power and Energy Systems Asiapes 2012, 164-169, 2012
dc.identifier.doi10.2316/P.2012.768-092
dc.identifier.other2-s2.0-84861935055
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4241
dc.sourceProceedings of the IASTED Asian Conference on Power and Energy Systems Asiapes 2012
dc.subjectParticle swarm optimization
dc.subjectSupport vector machine
dc.subjectThyristor controlled series capacitor
dc.titlePSO-based learning of support vector machines for adaptive TCSC
dc.typeConference Paper

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