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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
de Oliveira, Roger Alves
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
ORCID iD:
0000-0001-5845-5620
Nakhodchi, Naser
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
ORCID iD:
0000-0001-7528-101X
De Souza Salles, Rafael
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
ORCID iD:
0000-0002-3625-8578
Rönnberg, Sarah K.
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 10324
Conference 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-103655
DOI:
10.1049/icp.2023.0433
Scopus ID:
2-s2.0-85181541951
OAI: oai:DiVA.org:ltu-103655
DiVA, 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:
2024-02-08
Bibliographically approved
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de Oliveira, Roger Alves
Nakhodchi, Naser
De Souza Salles, Rafael
Rönnberg, Sarah K.
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Rönnberg, Sarah K.
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