Using Four-Level NSVM Technique to Improve DVC Control of a DFIG Based Wind Turbine Systems

Abstract

Traditional direct vector control (DVC) compositions which consist of proportional-integral (PI) regulators of a doubly fed induction generator (DFIG) driven have several disadvantages such as parameter variation problem, low dynamic performances and poor robustness. Therefore, based on examination of the DFIG model supplied by new modulation method, this work addresses a four-level space vector modulation (SVM) based on neural networks (NSVM). The conventional DVC control with SVM strategy has large ripples on the electromagnetic torque, harmonic distortion of rotor current, stator reactive and active powers developed by the DFIG-based wind turbine systems (WTSs). In order to resolve these problems, the DVC technique with NSVM strategy is proposed. Simulation results show the effectiveness of the proposed control technique especially in electromagnetic torque, power ripples and robustness against parameters variations.