Reduced complexity model predictive direct power control for unbalanced grid

Abstract

The model predictive control (MPC) group is frequently used as a controller in PWM rectifiers because of its accuracy. Yet, the requirement of a processor with high computational efficiency for its real-time implementation has been a significant drawback. Although a few low-complexity MPCs have been reported in the past, none of such methods are validated in an unbalanced grid. Hence, this work aims to propose a reduced-complexity MPC algorithm that can also handle an unbalanced grid. The conventional MPC evaluates the cost function for all the switching vectors and then chooses the switching vector that produces the minimum cost. In contrast, by equating its first-order derivative of the cost function to 0, the proposed analytical approach directly evaluates switching vectors, resulting in a minimum value of the cost function. Thus, the controller estimates only the closest possible solutions of the cost function minimization process and abandons the rest. Furthermore, the proposed method also endorses the use of additional non-linear constraints in the cost function, which eradicates one of the major drawbacks of the previous low-complexity techniques. To ascertain the practicability of the proposed method, it is examined in the MATLAB/Simulink environment and the real-time system. The obtained results look promising as the proposed controller is able to imitate the performance of conventional MPC with less computational burden.