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Statistical model checking (SMC) is used as a method for examining system be havior in all possible scenarios, and it has been successfully implemented in embedded automotive systems, sensor networks, and aeronautics and communication systems for solving problems that were beyond the abilities of traditional formal techniques. Using this approach, the system is simulated for a finite number of times, and, afterward, the samples are used for hypothesis testing. We will demonstrate how the area of application can also be extended to power electronics systems-in particular, how this method can be used to check the performance of finiteset controlled power electronic converters. [ABSTRACT FROM AUTHOR]

A new approach to performance validation of finite control set model predictive control (FCS-MPC) regulated power electronics converters is presented in this paper: statistical model checking (SMC). SMC is an established method used in various sectors of industry to provide solutions to problems that are beyond the abilities of classical formal techniques. The method is simple for implementation and requires only an operational system model that can be simulated and checked against properties. The approach will be presented on a standard two-level voltage source converter regulated by the FCS-MPC algorithm. In UPPAAL SMC toolbox, the converter system and controller are modeled as a network of stochastic timed automata. To assess the quality of the model, an equivalent Simulink model is used as a benchmark model. Using the model created in UPPAAL SMC toolbox the performance of the FCS-MPC algorithm is verified. The control algorithm is also tested on an experimental setup. During the evaluation, no significant degradation of reference tracking was found during transients nor under model parameter uncertainty. [ABSTRACT FROM AUTHOR]