Surrogate Thermal Model for Power Electronic Modules using Artificial Neural Network

Xu, Zhigen; Gao, Yuan; Wang, Xin; Tao, Xiaoyu; Xu, Qingui

Abstract

Virtual prototyping of power electronic modules aims to allow rapid evaluation of potential designs without building and testing physical prototypes. Among the interests in thermal models of the virtual modules, process of compact thermal models needs effective methodology to fast generate small models describing the thermal performance of a potential design. This study chooses the Generalized Minimized Residual (GMRES) Algorithm to process thermal models due to its efficiency. Based on that, a machine learning aided surrogate model is proposed for the prediction of thermal performance since existing approaches take much time to determine the thermal response to a particular input power. This surrogate model is created by training a dedicated artificial neural network (ANN) on simulation data, after that this model can quickly map the module temperature and the power input in time domain. In the training process, cross-validation method is introduced to determine which neuron structure should be selected for the practical data generated by thermal equations. The test group is noted in cross-validation to give the prediction performance of structure candidates. To verify the proposed method, the resulting data of trained surrogate models are compared with the accurate simulation data after the ANN based cross-validation.

Citation

Xu, Z., Gao, Y., Wang, X., Tao, X., & Xu, Q. (2019, October). Surrogate Thermal Model for Power Electronic Modules using Artificial Neural Network. Presented at IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal

• Presentation Conference Type: Edited Proceedings
• Conference Name: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
• Start Date: Oct 14, 2019
• End Date: Oct 17, 2019
• Acceptance Date: Oct 14, 2019
• Online Publication Date: Dec 9, 2019
• Publication Date: 2019-10
• Deposit Date: Oct 31, 2019
• Publicly Available Date: Nov 1, 2019
• Series ISSN: 2577-1647
• Book Title: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
• ISBN: 978-1-7281-4879-3
• DOI: https://doi.org/10.1109/IECON.2019.8927494
• Keywords: Artificial Neural Network (ANN); cross-validation; power electronic device (PED); thermal model; Generalized Minimized Residual (GMRES)
• Public URL: https://nottingham-repository.worktribe.com/output/3000912
• Publisher URL: https://ieeexplore.ieee.org/document/8927494
• Related Public URLs: https://iecon2019.org/; https://ieeexplore.ieee.org/xpl/conhome/1000352/all-proceedings
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Oct 31, 2019