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Author: search.filters.author.Pahasa, JonglakSubject: search.filters.subject.Particle swarm optimization

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    Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC
    (2017-03-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model- based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.
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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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    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.