Roentgenogram of the Month

Abstract

This paper proposes a Neural Input–Output Feedback Linearization (N-IOFL) controller for a Doubly Fed Induction Generator (DFIG) prototype connected to the grid. Under unbalanced grid voltages, the existing control strategies need to be modified, becoming very complicated. By using an identifier based on Recurrent High Order Neural Network (RHONN), trained on-line with an Extended Kalman Filter (EKF), an adequate model of the DFIG and of the DC-link can be obtained, which helps the control law based on feedback linearization to reject grid disturbances appearing under non-ideal grid conditions without decomposition process. Based on such identification, the proposed controller is used to track a desired Direct Current (DC) voltage reference at the output of DC-link, to maintain constant the electric power factor controlled by the Grid Side Converter (GSC), and to force independently the rotor currents to track a specified reference defined from the required stator powers, controlled by the Rotor Side Converter (RSC), under both balanced and unbalanced grid conditions. Real-time results illustrate the effectiveness of the proposed controller even in presence of non-ideal grid conditions.