Efficient Design Method for a Forward-Converter Transformer Based on a KNN–GRU–DNN Model

This letter proposes an efficient design method for a forward-converter transformer (FCT) with artificial intelligence (AI). Conventional FCT design is inefficient because it requires numerous repeated design processes. To solve this problem, this letter proposes FCT design by applying a K-nearest neighbors, gated recurrent unit, and deep neural network model. The design estimation accuracy of the proposed AI model was over 91% based on Google colaboratory validation. The proposed transformer design also satisfied the design requirements with less than 1,450 epochs. Once the learning process is completed, the proposed AI-based transformer design can obtain various FCT designs without further repeated training procedures. To verify the proposed design results, this letter conducted finite-element method (FEM) simulations using ANSYS Electronics Desktop 2018.2 and hardware-in-the-loop (HIL) experiments using OPAL-RT with the transformer design values resulting from the AI-based design model. According to the FEM simulations and HIL experiments, it is verified that the secondary winding induced voltage of the transformer designed by the AI-based model satisfies the design requirements.