Improving reliability assessment of transformer thermal top-oil model parameters estimated from measured data

This paper presents a methodology for assessing the reliability of thermal-model parameters for transformers estimated from measured data. The methodology uses statistical bootstrapping to calculate confidence levels (CL) and confidence intervals (CI). Bootstrapping allows us to make a small dataset look statistically larger, which allows a precise estimate of the transformer thermal model's reliability. The proposed methodology is tested on a 167-MVA oil-forced air-forced transformer. The CIs are evaluated with and without bootstrapping and the reliability indices are compared. The results show that the CI and CL values with bootstrapping are more consistently reproducible than the ones derived without bootstrapping.