Optimal Modulation of Triple Active Bridge Converters by an Artificial-Neural- Network Approach

Abstract

Isolated multiport converters can host loads and sources at different power and voltage levels to their ports by a single topology, giving potential merits in terms of power density and efficiency. However, the higher the number of ports, the higher the number of degrees of freedom in the modulation patterns. This high number of modulation variables complicates the optimization problem, making closed-form solutions impractical. This article avoids the analytic solution to the optimization problem by proposing a data-driven solution. The presented approach is based on an artificial neural network (ANN) trained to minimize the rms value of the currents flowing through the switches and the transformer windings of a triple active bridge (TAB) converter. This minimization is achieved by determining suitable values of the duty-cycles for modulating the converter switches. The proposed ANN-based modulation is validated considering an experimental TAB prototype rated $5 ,\mathrm{k}\mathrm{W}$.