An Adaptive-controlled Superconducting Magnetic Energy Storage Unit for Stabilizing a Grid-connected Wind Generator

Abstract This 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 huge impact on fault analysis. An over-voltage protection scheme is connected across the DC-link capacitor for over-voltage protection. The validity of the proposed control scheme is verified with the simulation results which are performed using the standard PSCAD/EMTDC environment.