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With the increasing penetration of distributed energy resources into islanded microgrids, a grid-forming inverter (GFI) has become the key element interfacing renewable energy sources. Usually, the GFI is employed with an output filter to minimize the harmonic content achieving high-quality output voltage regulation. To this end, model-predictive control (MPC) has been widely proposed to control the output voltage of GFI systems due to the high-quality performance, fast control response, and straightforward handling of constraints. However, an accurate model of the system is required for the conventional MPC to avoid a suboptimal performance under uncertainties. To overcome this known drawback, a novel model-free predictive control is proposed in this article. Consequently, the output voltage of an $LCL$ -filtered GFI is regulated without the knowledge of the physical model. [ABSTRACT FROM AUTHOR]

It is well known that predictive control methods can be affected by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties is a recurrent concern at the moment of evaluating the viability of this method 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 control in a three-phase two-level inverter. The analysis shows that the prediction error is not only determined by parametric mismatch but also by the instantaneous values of load current and inverter output voltage. This implies that within each sampling period of the predictive algorithm several conditions of prediction error are generated, as multiple voltage vectors are evaluated. Simulation and experimental results are provided and discussed showing the effects 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 fast transient step response is less affected by parameter changes. [ABSTRACT FROM PUBLISHER]

This article studies the model predictive control (MPC) for a twisted buck–boost inverter based on unfolding circuit. The focus is on the practical implementation of the MPC algorithm for the microcontroller designed for application in power electronics. Selection of proper cost function parameters along with a continuous control set reduced prediction horizon, at the same time keeping good quality of the grid current. The results showed that simplified differential equations and a multicore microcontroller contribute to the sample time reduction, which in turn increases the sampling frequency with the corresponding increase in the output current quality. The simulation and experimental results confirmed theoretical predictions. In conclusion, the MPC technique suits for reducing zero-crossing distortion and in applications based on unfolding circuit. [ABSTRACT FROM AUTHOR]