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Author: search.filters.author.Pahasa, JonglakSubject: search.filters.subject.Least squares support vector machine

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    A heuristic training-based least squares support vector machines for power system stabilization by SMES
    (2011-10-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This 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.
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    Least squares support vector machine for power system stabilizer design using wide area phasor measurements
    (2011-07-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes a design method of power system stabilizer by a least squares support vector machine (LS-SVMPSS) for wide area stability control. Both local and inter-area data based on synchronized phasor measurements considering time delay are considered as the input features of the LS-SVMPSS. A large number of the training data sets of a multi-machine power system are reduced by the measurement of similarity between samples. Removing the redundant data in the training set not only improves the LS-SVMPSS performance but also decreases computation expense during the operation of LS-SVMPSS. The LS-SVMPSS parameters and the similarity threshold are optimized by a genetic algorithm. As a result, the redundant data in the training set can be discarded while the reduced data are the optimal support vectors in the LS-SVMPSS model. The LS-SVMPSS control signals can be adapted in real time by various operating conditions and different disturbances. The performance of the LS-SVMPSS is compared with the conventional PSS and the neural network-based PSS. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVMPSS is very robust to various disturbances under wide range of operating conditions in comparison to other PSSs. © 2011 ISSN.
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    Wide-area SMES controller design using least-squares support vector machines
    (2010-12-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper presents an adaptive wide-area damping controller of superconducting magnetic energy storage (SMES) using the least squares support vector machine (LS-SVM). The LS-SVM for SMES controllers are trained using wide-area control signal obtained from synchronized phasor measurements considering time delay. 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 wide range of operating conditions in comparison to the conventional SMES.