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

Abstract

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