Abstract
In this work, the application of a feed-forward artificial neural network (FFANN) in
predicting the degree of polymerization (DP) and loss of life (LOL) in oil-submerged transformers by
using the solid insulation evaluation method is presented. The solid insulation evaluation method is
a reliable technique to assess and predict the DP and LOL as it furnishes bountiful information in
examining the transformer condition. Herein, two FFANN models are proposed. The first model is
based on predicting the DP when only the 2-Furaldehyde (2FAL) concentration measured from oil
samples is available for new and existing transformers. The second FFANN model proposed is based
on predicting the transformer LOL when the 2FAL and DP are available to the utility owner, typically
for the transformer operating at a site where un-tanking the unit is a daunting and unfeasible task.
The development encompasses constructing numerous FFANN designs and picking networks with
superlative performance. The training and testing procedures databank is based on the dataset of the
2FAL and DP from a fleet of transformers and measured from laboratory analysis. The correlation
coefficient of 0.964 was ascertained when the DP was predicted using the 2FAL measured in oil. In
the FFANN model, a correlation coefficient of 0.999 against the practical data where one can make a
reliable prediction of transformer LOL concerning 2FAL was generated and the amount of DP present
produced. This model can be used to predict the DP and LOL of new and existing transformers at the
manufacturer’s premises and operating in the field, respectively. To the knowledge of the authors, no
research work has been published addressing the methods proposed in this work.