Multilevel inverters using finite set- model predictive current control for renewable energy systems applications

Abstract

This research focuses on the predictive current control of multilevel converters with the aim of providing an optimized system for three-phase, multilevel inverters (MLIs) so that the load current and the voltage of the capacitors can be controlled. A model predictive current control algorithm is proposed, specifically directed at the utilisation of power obtained from renewable energy systems (RESs). The model was developed for three-phase, multilevel voltage source inverters (MLVSIs), three-phase, three-level diode-clamped converters (DCCs) and flying capacitor converters (FCCs). In this study the renewable energy systems model is used to investigate system performance when power is supplied to a resistiveinductive load (RL-load). The proposed control method was split into two different control algorithms. Firstly, a finite set-model predictive current control (FS-MPCC) method was developed to control the output current of three-phase, MLIs. This control method was selected to reduce the calculation effort for model predictive control (MPC) and to increase the possible prediction horizon. Secondly, to solve the flying capacitor voltage balance problem in an FCC, as well as to solve the DC-link capacitor voltage balance problem in a DCC, a hysteresis-voltage alancing algorithm based on predictive control, was designed—this algorithm was used to keep the flying capacitor voltages and DC-link capacitor voltages within their hysteresis bands. Finally, for some classes of power converters, a performance evaluation of the FS-MPCC method for three-phase, three-level MLIs was investigated in terms of power quality and dynamic response. The improvement was assessed in terms of total harmonic distortion (THD) of the output voltage for the RL-load. The modelling and co-simulation were carried out using MATLAB/Simulink with PSIM software. The co-simulation results indicated that the proposed control algorithms achieved both high performance and a high degree of robustness in RESs applications.