Artificial Neural Networks-Based Multi-Objective Design Methodology for Wide-Bandgap Power Electronics Converters

Design methodology of power electronics converters is critical to fully explore the potential of wide-bandgap power semiconductors at the converter level. However, existing design methods largely rely on complex mathematical models which significantly increases the computational time, complexity and further leads to problems including poor constraint handling capabilities, inaccurate design, difficult parameter tuning and inadequate problem dimension. These all could generate sub-optimal designs that make the whole design process meaningless. To overcome the aforementioned problems, in this paper, an artificial neural network (ANN)-based multi-objective design approach is proposed, which offers significant advantages in reducing the repetitive usage of complex mathematical models and hence the computational time of design. The computational time was reduced by up to around 78% and 67% compared to the numerical modeling and geometric program (GP) methods as validated through a hardware design process. The proposed method was implemented in MATLAB/Simulink to design a 1 kW single-phase inverter, resulting in a design with an optimized efficiency (98.4%) and power density
$\mathbf {(\mathrm{\text{4.57}\,kW/dm^{3}})}$
. The accuracy of the design is verified through experimental prototyping and the measured efficiency and power density are 98.02% and
$\mathrm{\text{4.54}\,kW/dm^{3}}$
, respectively, so the errors of efficiency and power density are both less than 1%.