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Author: search.filters.author.Dechanupaprittha, S.Subject: search.filters.subject.Superconducting magnetic energy storage

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    Stabilization of tie-line power oscillations by robust SMES in interconnected power system with large wind farms
    (2009-12-16)
    Ngamroo, I.
    ;
    Cuk Supriyadi, A. N.
    ;
    Dechanupaprittha, S.
    ;
    Mitani, Y.
    This paper proposes a robust controller design of Superconducting Magnetic Energy Storage (SMES) for stabilization of interconnected power systems with wind farms. The inverse additive perturbation is applied to represent system uncertainties such as variation of system parameters, several generating and loading conditions etc. The structure of active and reactive power controllers of SMES is the first-order lead-lag compensator. To tune the controller parameters, the optimization problem is formulated based on the enhancement of additive stability margin. The particle swarm optimization is used to solve for controller parameters. Simulation studies in a six-area interconnected power system with wind farms confirm the robustness of the proposed SMES against various system operating conditions.
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    A heuristic-based design of fuzzy SMES controller for stabilization of interconnected power system
    (2006-12-01)
    Dechanupaprittha, S.
    ;
    Mitani, Y.
    ;
    Watanabe, M.
    ;
    Hongesombut, K.
    ;
    Ngamroo, I.
    This paper presents a heuristic-based design of fuzzy logic controller for a superconducting magnetic energy storage (SMES) for stabilization of interconnected power system. Nowadays, a power system becomes complex due to increasing demand in electricity and power system network expansion. Subject to major disturbances, occurrence of power system oscillations is unavoidable. To alleviate the problem, the fuzzy SMES controller is applied in this paper. In particular, SMES has a simultaneous control scheme of active and reactive power. Moreover, the heuristic method, i.e. hybrid tabu search and evolutionary programming (Hybrid TS/EP) is employed to simultaneously optimize control rules and membership functions for fuzzy SMES controller. Consequently, the simulation results and evaluations based on a two-area four-machine power system are carried out to examine the performance and effectiveness of the designed fuzzy SMES controller.