Robust Predictive Stator Current Control Based on Prediction Error Compensation for a Doubly Fed Induction Generator Under Nonideal Grids

Abstract

Traditional model-based predictive current control (MBPCC) for a doubly fed induction generator (DFIG) is easily affected by machine parameter variations. Recently, a model-free predictive current control (MFPCC) was proposed to enhance the parameter robustness of DFIG systems by using the past current difference to predict the future current difference under the same switching state. However, this approach has the problems of high sampling frequency, variable switching frequency, and high steady ripples. To solve these problems, this article proposes a robust predictive stator current control (RPCC) method for DFIGs. Different from the prior MFPCC, the current error between the measured value and the predicted value is not only used in the stage of the stator current prediction, but also in the calculation of rotor voltage vector. Hence, the performance deterioration caused by machine parameter variations is significantly alleviated. Furthermore, a support vector machine can be combined with the proposed RPCC. In this way, the proposed method ensures strong parameter robustness and small steady-state power ripples. Finally, by recalculating the current reference value, the method can also achieve a good performance even under nonideal grids. The proposed RPCC is compared with MBPCC and MFPCC and its effectiveness is confirmed by the presented simulation and experimental results.