Discernment of transformer oil stray gassing anomalies using machine learning classification techniq...
Discernment of transformer oil stray gassing anomalies using machine learning classification techniques
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a particular unit during service may interrupt a ma...
Alternative Titles
Full title
Discernment of transformer oil stray gassing anomalies using machine learning classification techniques
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_9dabc1cac3ad4993bea73d4a3dc41f27
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9dabc1cac3ad4993bea73d4a3dc41f27
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-50833-7
