Transformer thermal modeling: improving reliability using data quality control

Eventually, all large transformers will be dynamically loaded using models updated regularly from field-measured data. Models obtained from measured data give more accurate results than models based on transformer heat-run tests and can be easily generated using data already routinely monitored. The...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2006-07, Vol.21 (3), p.1357-1366
Main Authors: Tylavsky, D.J., Xiaolin Mao, McCulla, G.A.
Format: Article
Language:English
Subjects:
ANSI C57.91
Applied sciences
Cooling
Electrical engineering. Electrical power engineering
Error correction
Errors
Exact sciences and technology
Heat transfer
Load modeling
Mathematical models
Monitoring
Nonlinearity
Predictive models
Quality control
Screening
Temperature measurement
Testing
Thermal loading
top-oil temperature
transformer
transformer thermal modeling
Transformers
Transformers and inductors
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Summary:Eventually, all large transformers will be dynamically loaded using models updated regularly from field-measured data. Models obtained from measured data give more accurate results than models based on transformer heat-run tests and can be easily generated using data already routinely monitored. The only significant challenge to use these models is to assess their reliability and improve their reliability as much as possible. In this work, we use data-quality control and data-set screening to show that model reliability can be increased by about 50% while decreasing model prediction error. These results are obtained for a linear model. We expect similar results for the nonlinear models currently being explored.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2005.864039