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• W1524196556 abstract "AbstractThis article introduces an adaptive artificial neural network controlled superconducting magnetic energy storage with the purpose of enhancing the dynamic stability of a wind generator that is connected to the electric grid. The control strategy of the superconducting magnetic energy storage unit depends on the cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated-gate bipolar transistors. The proposed controller is used to control the duty cycle of the DC-DC chopper. The modeling of the system and its control techniques are presented. The effectiveness of the proposed adaptive artificial neural network controlled superconducting magnetic energy storage is then compared with that of a conventional proportional-integral controlled superconducting magnetic energy storage. This study concentrates on symmetrical and unsymmetrical faults. A two-mass drive train model of the wind generator is utilized in the analyses for realistic responses as it has a hu..." @default.
• W1524196556 created "2016-06-24" @default.
• W1524196556 creator A5010659601 @default.
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• W1524196556 date "2015-05-11" @default.
• W1524196556 modified "2023-10-18" @default.
• W1524196556 title "An Adaptive-controlled Superconducting Magnetic Energy Storage Unit for Stabilizing a Grid-connected Wind Generator" @default.
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• W1524196556 doi "https://doi.org/10.1080/15325008.2015.1010663" @default.
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