Abstract
This article proposes a novel class of neural-network-inspired statistical data-driven models, especially derived for the purpose of design optimization of medium-frequency transformers. These models allow for an efficient [three to four orders of magnitude faster compared to a finite-element method (FEM)] yet sufficiently accurate (within 5–10% error relative to the FEM) and numerically stable estimation of the complex effects, with otherwise impractically high computational cost and/or convergence issues. The application of the proposed modeling framework is described in detail on two characteristic examples of the complex electromagnetic phenomena occurring within the medium-frequency transformers. The performance of the derived models is verified both with detailed FEM simulations and experimental results.
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