Modular Modeling and Statistical Validation for Grid-Connected FS-MPC-Controlled Matrix Converters

In recent publications, statistical model checking (SMC) has been proposed as a method for verifying the performance of finite-set model predictive control (FS-MPC) algorithms applied to power electronics converters. One of the reasons the full potential of the method in the power electronics systems (PESs) has not yet been explored is the time-consuming modeling process. In this article, we propose a modular method of modeling the power electronics system components by providing simple building blocks, which can be connected to build different PESs. The modeling method is here demonstrated on a direct matrix converter, which operates in a stochastic grid with different harmonic distortion levels and voltage sags. By applying the SMC, the performance of the control algorithm in terms of the output current distortion, effects of the weighting factor selection, and grid distortions on the device utilization can be evaluated. The obtained results confirm that high grid distortions and voltage sags will increase the stress of several devices. This information can be of great importance to identify the most stressed components and how the control algorithm can be adapted to extend the lifetime of the components and thereby the system during different grid conditions. The verified FS-MPC algorithm has also been implemented in an experimental setup.