Power transformers are supposed to be an expensive and critical component of a power system and so its schedule maintenance is an important aspect near
the utilities. The cellulose paper used as the solid insulating material of the transformer deteriorates regularly due to progressive aging. As a result, it produces several
degradation by-products of cellulose insulation into the transformer oil. Furfurals are
among the major by-product of cellulose and are exploited to estimate the physical
state of the transformer’s dielectric and the electrical insulation directly and noninvasively. In the present work, an intelligent system is proposed and developed that
predicts the level of furfural in the transformer oil. The system makes predictions
using easily quantifiable parameters, enabling utilities to avoid suffering financial
losses. The proposed system employs the Adaptive Neuro Fuzzy Inference System
(ANFIS) technique with temperature and moisture as the input and 2-Furfuraldehyde
