An Active Learning Framework for Reliability Oriented Power Electronics Design

An Active Learning Framework for Reliability Oriented Power Electronics Design | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 February 2025 V1 Latest version Share on An Active Learning Framework for Reliability Oriented Power Electronics Design Authors : Xinyue Zhang , Xin Zhao , Jie Kong , Jiacheng Sun , Xiaohua Wu , Chaoqiang Jiang , and Yi Zhang 0000-0003-0248-7644 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173887054.44331114/v1 Published IEEE Transactions on Industrial Electronics Version of record Peer review timeline 472 views 280 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper proposes an active learning framework to address a longstanding research question: how much data is needed for data-driven power electronics designs. The proposed method can automatically explore the optimal scenario using minimal data while achieving the desired accuracy. To demonstrate the proposed method, the reliability-oriented design (ROD) for a traction converter is used as a case study. While traditional methods achieving ROD necessitate extensive simulation and experiments, the proposed method uses surrogate models and active learning to achieve the desired accuracy with the minimal computational requirements. The performance of the proposed method has been verified by experimental measurements. The computation cost is reduced by 62.5% compared to the traditional ROD method. Supplementary Material File (surrogate_model.pdf) Download 10.39 MB Information & Authors Information Version history V1 Version 1 06 February 2025 Peer review timeline Published IEEE Transactions on Industrial Electronics Version of Record 1 Dec 2025 Published Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords active learning optimization power module reliability design surrogate model Authors Affiliations Xinyue Zhang View all articles by this author Xin Zhao View all articles by this author Jie Kong View all articles by this author Jiacheng Sun View all articles by this author Xiaohua Wu View all articles by this author Chaoqiang Jiang View all articles by this author Yi Zhang 0000-0003-0248-7644 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 472 views 280 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xinyue Zhang, Xin Zhao, Jie Kong, et al. An Active Learning Framework for Reliability Oriented Power Electronics Design. Authorea . 06 February 2025. DOI: https://doi.org/10.22541/au.173887054.44331114/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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