Artificial neural networks-based multi-objective design methodology for wide-bandgap power electronics converters

Rajamony, Rajesh, Wang, Sheng ORCID: https://orcid.org/0000-0002-2258-2633, Calderon-Lopez, Gerardo, Ludtke, Ingo and Ming, Wenlong ORCID: https://orcid.org/0000-0003-1780-7292 2022. Artificial neural networks-based multi-objective design methodology for wide-bandgap power electronics converters. IEEE Open Journal of Power Electronics 3 , pp. 599-610. 10.1109/OJPEL.2022.3204630

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview

Abstract

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 (4.57kW/dm3) . The accuracy of the design is verified through experimental prototyping and the measured efficiency and power density are 98.02% and 4.54kW/dm3 , respectively, so the errors of efficiency and power density are both less than 1%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2644-1314
Funders:	EPSRC
Date of First Compliant Deposit:	24 September 2022
Date of Acceptance:	28 August 2022
Last Modified:	12 Jun 2023 19:23
URI:	https://orca.cardiff.ac.uk/id/eprint/152819

Actions (repository staff only)

Edit Item