Grid-Voltage Sensorless Model-Free Predictive Current Control for PWM Rectifiers With Measurement Noise Suppression

Abstract

This article investigates the performance of model-free predictive current control (MFPCC) with different sampling frequencies and measurement-noise conditions. It shows that the performance of MFPCC can be degraded seriously even if the stagnant current variation update is removed because the current variations for the MFPCC design are highly sensitive to measurement noise, especially at high sampling frequency. In order both to overcome this problem and to remove the stagnant current variation update, we propose an enhanced MFPCC for pulsewidth modulation rectifiers. In the proposed method, the stagnant current variation update is removed by updating the intermediate terms to reconstruct all current variations, while noise involved in the current variations is suppressed with the aid of an second-order generalized integrator filter. As a result, high control performance is achieved with the proposed method even if a very high sampling frequency is used, in contrast to previous MFPCC methods. In addition, a new grid-voltage sensorless technique was also developed for the proposed MFPCC. Simulation and experiment results under both normal and distorted grid conditions are provided to verify the effectiveness and robustness of the proposed method.