Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers

Abstract

Insulation is an essential part of power transformers, which guarantees an efficient and reliable operational life. It mainly consists of mineral oil and insulation paper. Most of the major failures of power transformers originate from internal insulation failures. Monitoring aging and thermal behaviour of the transformer’s insulation paper is achieved by different techniques, which consider the Degree of Polymerization (DP) to evaluate the cellulose degradation and other chemical factors accumulated in mineral oil. Given the physical and chemical nature of the problem of degradation, we couple it with machine learning models to predict the desired parameters for considered equations. In particular, the equation used applies the Arrhenius relation, which comprises parameters like the pre-exponential factor, which depends on the cellulose’s contamination content, and the activation energy, which is connected to the temperature dependence; both of the factors need to be estimated for our problem. For this reason, Physics-Informed Neural Networks (PINNs) are considered for solving the data-driven discovery of the DP equation.