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 only significant challenge to using 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.

Eventually, all transformers greater than about 20 MVA will be loaded using dynamic thermal models and these models will be derived from measured field data rather than from the data contained in heat-run reports.

The comparison of the top-oil temperature (TOT) performance of the traditional (Clause 7) ANSI/IEEE model [1] versus a model derived from field-measured data shows why this will be the case: even simple linear models derived from field-measured data are more accurate than the ANSI Clause 7 model using parameters taken from transformer test (heat-run) reports. (See Fig. 1 and Fig. 2 for this comparison.

Also see Appendix A for a description of this transformer and thermal sensors used in generating this data.) Indeed, models derived from measured field data—data which utilities already routinely monitor and record—naturally account for many phenomena in operating transformers (operational faults such as fouled heat exchangers, inoperative pumps/fans, etc.) that the nominal Clause 7 model does not.

And it is not a simple matter to detect the type of operational fault that has occurred and then, for the Clause 7 model, adjust the model appropriately.
Source: Power Systems Engineering Research Center
Author: Daniel J. Tylavsky | Xiaolin Mao | Gary A. McCulla

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