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Prediction of on-load tap-changer switch time from vibroacoustic measurements by machine learnings
Hitachi Energy Research,Forskargränd 7, 721 78 Västerås,Sweden;Uppsala University, Department of Electrical Engineering,Box 65, 751 03 Uppsala,Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Department of Information Technology,Box 337, 751 05 Uppsala,Sweden.ORCID iD: 0000-0002-2678-1330
Hitachi Energy Research,Forskargränd 7, 721 78 Västerås,Sweden;Uppsala University, Department of Information Technology,Box 337, 751 05 Uppsala,Sweden.
Hitachi Energy Research,Forskargränd 7, 721 78 Västerås,Sweden.
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2024 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
2024. Vol. 2023, no 46, p. 350-354
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-547375DOI: 10.1049/icp.2024.0524OAI: oai:DiVA.org:uu-547375DiVA, id: diva2:1927900
Conference
23rd International Symposium on High Voltage Engineering (ISH 2023)
Available from: 2025-01-15 Created: 2025-01-15 Last updated: 2025-01-17Bibliographically approved

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Mattsson, Per

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