A heuristic training-based least squares support vector machines for power system stabilization by SMES

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dc.contributor.authorPahasa, Jonglak
dc.contributor.authorNgamroo, Issarachai
dc.date.accessioned2026-08-06T10:02:52Z
dc.date.available2026-08-06T10:02:52Z
dc.date.issued2011-10-01
dc.description.abstractThis paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES. © 2011 Elsevier Ltd. All rights reserved.
dc.identifier.citationExpert Systems with Applications, 38(11), 13987-13993, 2011
dc.identifier.doi10.1016/j.eswa.2011.04.206
dc.identifier.issn09574174
dc.identifier.other2-s2.0-79960027307
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/3797
dc.sourceExpert Systems with Applications
dc.subjectInter-area oscillation
dc.subjectLeast squares support vector machine
dc.subjectParticle swarm optimization
dc.subjectSimilarity measurement
dc.subjectSuperconducting magnetic energy storage
dc.titleA heuristic training-based least squares support vector machines for power system stabilization by SMES
dc.typeArticle

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