Model-based predictive direct power control of doubly fed induction generators

This paper presents a predictive direct power control strategy for doubly fed induction generators (DFIGs). The method predicts the DFIG's stator active and reactive power variations within a fixed sampling period, which is used to directly calculate the required rotor voltage to eliminate the power errors at the end of the following sampling period. Space vector modulation is then used to generate the required switching pulses within the fixed sampling period that results in a constant switching frequency. The impact of sampling delay on the accuracy of the sampled active and reactive powers is analyzed, and detailed compensation methods are proposed to improve the power control accuracy and system stability. Experimental results for a 1.5-kW DFIG system demonstrate the effectiveness and robustness of the proposed control strategy during power steps, and variations of rotating speed and machine parameters. System performance for tracking varying stator power references further illustrates the dynamic performance of the proposed method