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Author: search.filters.author.Pahasa, JonglakSubject: search.filters.subject.Superconducting magnetic energy storage

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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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    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.
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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.