An Improved Model-free Predictive Power Control for Three-Phase AC/DC Converters

Abstract

Model predictive power control (MPPC) is a powerful and popular control method for the control of three-phase AC/DC converters due to its merits of quick response and simple principle. However, conventional MPPC suffers from the high steady power ripples due to the application of only one voltage vector during one control period. Furthermore, the performance will deteriorate when there are parameter variations due to saturation, temperature, and so on. Recently, model-free predictive current control (MFPCC) based on current difference detection has been proposed to solve the problem of parameter robustness for the control of ac/dc converters. However, it cannot be directly applied to the power control of ac/dc converters due to the complicated relationship between the complex power and converter voltage. This paper proposes an improved model-free predictive power control (MFPPC) for three-phase ac/dc converters. Instead of using accurate mathematical model, the proposed method uses an ultra-local model to predict the complex power. Different from the conventional ultra-local model, both the gain of the input voltage and the other parts of the ultralocal model are estimated and updated online based on the input and output information of system. Hence, the proposed MFPPC is not affected by the parameter variations and exhibits strong parameter robustness. The effectiveness of the proposed method is confirmed by the presented simulation and experimental results.