Condition Diagnosis of Oil-Filled Transformers Based on Online DGA Data Using Machine Learning

Dissolved gas analysis in transformer oil (DGA) is one of the most informative methods for diagnosing the condition of power transformers. The use of online gas analyzers enhances the capabilities of DGA, but algorithms based on machine learning methods are required to process large data sets. This paper presents a comparative study of several classes of machine learning models for classifying transformer defects based on data from online gas analyzers. The models were trained on labeled data obtained from online gas analyzers installed on operating power transformers.