Transformer winding type recognition based on FRA data and a support vector machine model

Transformer winding type recognition based on FRA data and a support vector machine model

Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transfo...

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Bibliographic Details
Main Authors: Xiaozhou Mao, Zhongdong Wang, Peter Crossley, Paul Jarman, Andrew Fieldsend-Roxborough, Gordon Wilson
Format: Article
Language:English
Published: Wiley 2020-05-01
Series:High Voltage
Subjects:
support vector machines
sensitivity analysis
transformer windings
learning (artificial intelligence)
power transformer testing
ferroresonance
fault diagnosis
frequency response
voltage 13.0 kv
voltage 275.0 kv
voltage 400.0 kv
frequency response analysis
transformer fra databases
uk national grid fra database
transformer asset databases
fra measurement data
transformer winding type recognition
winding type
single helical windings
unknown winding information
fra traces
svm model
support vector machine model
Online Access:https://digital-library.theiet.org/content/journals/10.1049/hve.2019.0294

Internet

https://digital-library.theiet.org/content/journals/10.1049/hve.2019.0294

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