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

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    Two-Stage Optimization Based on SOC Control of SMES Installed in Hybrid Wind/PV System for Stabilizing Voltage and Power Fluctuations
    (2021-11-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    This paper proposes the two-stage optimization of superconducting magnetic energy storage (SMES) integrated into hybrid wind/photovoltaic (PV) generators considering the state of charge (SOC) control for stabilizing voltage and power fluctuations. The first stage aims to achieve the minimal coil inductance of SMES that guarantees the stored energy for system stabilization. In the second stage, the control parameters of SMES are optimized to keep the SOC at the desired level. As a result, the minimum coil inductance with sufficient stored energy of SMES for stabilizing system and regulating SOC at the target value can be obtained for entire period of operation. Study results in the distribution system with various loads ensure that the hybrid wind/PV with optimized internal SMES yields superior stabilizing performance in comparison with the SMES externally installed at the wind/PV terminal.
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    Optimal least squares support vector machines for SMES controller design using wide area phasor measurements
    (2012-07-01)
    Pahasa, Jonglak
    ;
    Ngamroo, Issarachai
    In this paper, the least squares support vector machines (LS-SVMs) based design of superconducting magnetic energy storage (SMES) controller is proposed for wide area stability control. The LS-SVMs for SMES controllers are trained by local and inter-area data based on synchronized phasor measurements considering time delay. A large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. The LS-SVM parameters and the similarity threshold are optimized by a particle swarm optimization. Subsequently, the redundant data in the training set can be discarded while the reduced data are the optimal support vectors in the LS-SVM model. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a six-area West Japan interconnected 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. Copyright © 2011 John Wiley & Sons, Ltd.