Model predictive control algorithm for multilevel inverter in high performance load

In the applications of high voltage renewable energy systems such as wind and solar farm the use of model predictive control (MPC) in the power conversion stage is very advantageous. The strict requirements of utility or transmission operators or high performance loads for standalone need a very sophisticated and robust control strategy. A three-phase, flying capacitor multilevel converter is considered in this research work for high performance standalone load modelled as a simple resistive-inductive load (RL-load). Two primary control objectives employed in this work are reference current tracking and flying capacitor voltage balancing, both of which are done concurrently by means of a multivariable control algorithm. A hysteresis-based algorithm is presented to balance the flying capacitor voltage in a complimentary way with the finite state-model predictive current control (FS-MPCC) algorithm. In the implementation stage of FS-MPCC for the control of a three-phase, three-level flying capacitor converter (FCC) for RL-load, the estimation step, predictive step and optimization step are performed. The performance of the proposed control is assessed by parameter mismatching between the model and the actual system parameters. Because of in nearly all systems, the parameters can change or are, to a degree, indeterminate. To evaluate the parameter sensitivity of the proposed control algorithm, different values for the load resistor and inductor are used. These values are estimated in order to determine how robustly the system could track the reference signal, balance the flying capacitor voltage, and the extent of the total harmonic distortion (THD). Co-simulation is effected by means of MATLAB/Simulink with PSIM software.