PROJECT TITLE :

Analysis of Finite-Control-Set Model Predictive Current Control With Model Parameter Mismatch in a Three-Phase Inverter

ABSTRACT:

It's well-known that predictive management strategies can be tormented by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties could be a recurrent concern at the instant of evaluating the viability of this methodology for power electronics applications. This paper proposes an analytic approach to examine the influence of model parametric uncertainties on the prediction error of FCS-MPC for current management during a 3-part 2-level inverter. The analysis shows that the prediction error isn't solely determined by parametric mismatch however conjointly by the instantaneous values of load current and inverter output voltage. This implies that among every sampling period of the predictive algorithm many conditions of prediction error are generated, as multiple voltage vectors are evaluated. Simulation and experimental results are provided and discussed showing the results of inaccuracies in the modeling of load resistance and inductance parameters on the performance of FCS-MPC. Even though steady-state performance is noticeably affected with parameter changes, especially when the load inductance is overestimated by the model, its quick transient step response is a smaller amount tormented by parameter changes.