A Technique for Transformer Remnant Cellulose Life Cycle Prediction Using Adaptive Neuro-Fuzzy Inference System

This article presents an ultramodern modelling algorithm for predicting the remnant cellulose life cycle oil-submerged power transformers based on adaptive neuro-fuzzy interference system (ANFIS). The polymer characteristics, degree of polymerization (DP), and 2-furaldehyde (2FAL) 100 were measured collated, which apportioned into 70 training databanks 30 as testing datasets. transformer was predicted using proposed ANFIS model characterized by DP 2FAL inputs. approach returns 98.23% 99.86% reliability. applied to 10 case studies in their cycle. To corroborate ANFIS, a comparative study carried out employing existing approaches studies, significant error margins observed. At large, results presented this certify dominance over compared models. furnishes pathway obliterate constraints classical techniques evaluating