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Author: search.filters.author.Ngamroo, IssarachaiSubject: search.filters.subject.Support vector machine

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    PSO-based learning of support vector machines for adaptive TCSC
    (2012-06-12)
    Pahasa, Jonglak
    ;
    Hongesombut, Komsan
    ;
    Ngamroo, Issarachai
    This 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.
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    PSO based kernel principal component analysis and multi-class support vector machine for power quality problem classification
    (2012-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    Electric 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.
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    GA-based support vector machines for adaptive power system damping controller of SMES
    (2010-07-30)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes the application of support vector machines (SVMs) to design of an adaptive power system damping controller for superconducting magnetic energy storage (SMES). A genetic algorithm is used to optimize the SVM parameters based on k-fold cross-validation. The SVMs for SMES controllers are trained by the data obtained from a multi-machine power system, and the optimal SVM parameters. The SVMs can be adapted by various operating conditions when the power system operates either inside or outside of the training set. Simulation results in a two-area four-machine power system demonstrate that the proposed SVMs for adaptive SMES is much superior to the conventional SMES controller with fixed parameters under various operating conditions and severe disturbances.