Evaluation of hottest-spot temperature models using field measured transformer data

The acceptability of two hottest-spot temperature models is assessed in this paper. The first model, contained in the IEEE loading guide, is shown not to accurately account for the effects of the top-oil temperature (TOT) variation on the hottest-spot temperature. A new model which accounts for TOT variation is derived. The original and modified models are linearized and fitted to field data using linear regression to obtain optimal parameter estimates. Comparison of the parameter estimates and prediction simulations show that even though the original model is not structurally accurate, it, as well as the modified model, are acceptable for prediction purposes. The method of nonlinear regression is also used in an attempt to find better parameter estimates for the nonlinear modified model. It is shown that parameter estimates for the nonlinear model are inferior to those obtained for the linear models.