Work

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

Year: 2023

Type: article

Abstract: Isolated multi-port 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 paper 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 5kW.

Source: IEEE Transactions on Industrial Electronics

DOI: 10.1109/tie.2023.3270529

Authors: Ahmed A. Ibrahim, Andrea Zilio, Tarek Younis, Davide Biadene, Tommaso Caldognetto, Paolo Mattavelli

Institutions: University of Padua, Aswan University

Language: English

Topic: Advanced DC-DC Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,345