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Deep Learning Graphical Tool Inspired by Correlation Matrix for Reporting Long-Term Power Quality Data at Multiple Locations of an MV/LV Distribution Grid
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-5845-5620
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-7528-101X
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-3625-8578
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-4004-0352
2023 (English)In: 27th International Conference on Electricity Distribution (CIRED 2023), IEEE, 2023, p. 609-613, article id 10324Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023. p. 609-613, article id 10324
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-103655DOI: 10.1049/icp.2023.0433Scopus ID: 2-s2.0-85181541951OAI: oai:DiVA.org:ltu-103655DiVA, id: diva2:1827905
Conference
27th International Conference and Exhibition on Electricity Distribution (CIRED 2023), Rome, Italy, June 12-15, 2023
Note

ISBN for host publication: 978-1-83953-855-1

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-02-08Bibliographically approved

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de Oliveira, Roger AlvesNakhodchi, NaserDe Souza Salles, RafaelRönnberg, Sarah K.

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