Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants
- Submitting institution
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University of Strathclyde
- Unit of assessment
- 12 - Engineering
- Output identifier
- 111816780
- Type
- D - Journal article
- DOI
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10.1109/TIE.2018.2860532
- Title of journal
- IEEE Transactions on Industrial Electronics
- Article number
- -
- First page
- 4726
- Volume
- 66
- Issue
- 6
- ISSN
- 0278-0046
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The methodology presented has been adopted by EDF Energy for independent transformer health lifetime evaluation (contact: Martin Kearns, Chief Electrical Engineer EDF). The research was also invited by industrialists from the North American Nuclear Industry for presentation at the joint Nuclear Innovation Institute (NII) and ANRC Showcase Event titled 'Asset Management and Industrial Informatics', Toronto, May 2019 (https://strath.eventsair.com/anrc/may-9th-2019), organised through Dominque Lachance, Senior Strategist, Engineering, Bruce Power. The paper was a key reference for securing a further £98k industry research funding on advanced transformer diagnostics from EDF and Bruce Power (PI: B G Stewart, Project ANRC 11-3).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -