Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters
Ahmed A. Ibrahim, Andrea Zilio, Tarek Younis, Davide Biadene, Tommaso Caldognetto, Paolo Mattavelli
Abstract
Isolated multi-port converters show the merits of hosting several sources and loads with different voltage and power ratings, allowing power routing among multiple ports with high power density. However, many degrees of freedom are available for modulation, and exploiting them for optimal converter operation is challenging. This paper proposes an artificial neural network (ANN) approach that minimizes the rms ports currents of a triple active bridge (TAB) converter for the entire range of operation. The ANN is trained to determine the optimum duty-cycles for total true rms current minimization. The effectiveness of the ANN implementation is shown by considering an experimental TAB converter prototype rated 5kW.