Review and Conceptual Design of FPGA-based Application for Data-Driven Power Electronic Systems

AI-based data-driven methods are an emerging research direction in the field of power electronics. However, because of the absence of large datasets, the development of these solutions have some barriers to overcome. To properly train machine learning algorithms and neural networks a large amount of training data is necessary. This dataset can be a union of simulation and measured data. Generating simulation data with computer simulations can be slow process and gathering real data is not cost-effective. Real-Time simulators based on FPGAs can be powerful tools to accelerate simulation, and create datasets for AI applications in a cost-effective and accurate way. In this paper the possible FPGA-based solutions, which can be applicable for the problems, have been reviewed. Their applicability have been discussed, moreover a simplified FPGA-based concept have been designed and embedded into two possible AI-based application area.