Abstract
Although various machine learning-based methods have been proposed for condition monitoring in power electronics, they are challenging to be implemented in practice due to the accuracy, data availability, computation burden, explainability, etc. Physics-informed machine learning (PIML) has been emerging as a promising direction where the above challenges can be mitigated by incorporating domain knowledge. In this paper, we propose a PIML- based parameter estimation method for a DC-DC Buck converter, as an exemplary application of PIML in power electronics. By seamlessly integrating a deep neural network and the converter physical model, it can estimate multiple component parameters simultaneously with high accuracy and robustness, while based on a limited dataset. It expects to provide a new perspective to tailor existing ML tools for power electronic applications.
Originalsprog | Engelsk |
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Publikationsdato | 2022 |
Antal sider | 6 |
Status | Udgivet - 2022 |
Begivenhed | 37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022 - Houston, USA Varighed: 20 mar. 2022 → 24 mar. 2022 |
Konference
Konference | 37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022 |
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Land/Område | USA |
By | Houston |
Periode | 20/03/2022 → 24/03/2022 |
Sponsor | IEEE Power Electronics Society (PELS), Industry Applications Society (IAS), Power Sources Manufacturers Association (PSMA) |
Bibliografisk note
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