Prediction of Polymerization Degree Using Machine Learning: New Methodology for Assessing the Lifespan of Power Transformers

EasyChair Preprint 11170

Abstract

The main indicator used to assess the condition of solid insulation in power equipment currently is the degree of polymerization (DP). This work presents a systematic study where machine learning techniques are used to estimate DP from 2-fal and other indicators. The results are promising, indicating that 2-fal, CO$_2$/CO, the Chendong formula, and equipment power can be used together to better predict the current value of its service life.

Keyphrases: Ativos, Envelhecimento, Grau de Polimerização, Transformador de Potência

@booklet{EasyChair:11170,
  author    = {Rafael Fehlberg and Daniel Carrijo and Gabriel Gomes and Sofia Lopes and Rogério Flauzino and Renan Santa Rosa and Iony Patriota},
  title     = {Prediction of Polymerization Degree Using Machine Learning: New Methodology for Assessing the Lifespan of Power Transformers},
  howpublished = {EasyChair Preprint 11170},
  year      = {EasyChair, 2023}}