The 3-phase Dual Active Bridge, DAB, converter has many advantages over the traditional single-phase DAB, such as higher efficiency and better current distribution. To achieve an optimal modulation, it is often necessary to modify the phase shift, as well as the duty cycles of the primary and secondary bridges. Due to the high complexity of the currents in the 3-phase Medium Frequency Transformer, MFT, and their complex relationship with the duty cycles it is necessary to utilize advanced optimization techniques. In this work it is proposed the use of an Artificial Neural Network, ANN, trained from data generated using the Multidimensional Ripple Correlation Technique, RCC. The use of the ANN instead the RCC ensures obtaining the optimal modulation at every point, thanks to its ability to generalize, without demanding high computational cost and without introducing slow dynamics, improving one of the main problems of the RCC technique.
.jpg "David Molinero photo")