Transformer Fault Detection Using DGA Based on Three Ratio Technique and Random Forest Algorithm

Power transformers are essential elements in the production and distribution of electricity, and keeping them in optimum operating condition is a constant concern for specialists in the field. The condition of power transformers is mainly determined by the condition of the mixed insulation system, i.e. solid cellulose paper insulation and liquid insulating oil insulation. This is why the Three Ratio Technique (TRT) is used with good results for the early detection of power transformer faults. In this paper, the ratios defined by the TRT method are used to train a machine learning classifier based on ensemble and random forest algorithms. The validation of the power transformer fault identification software application for the proposed method is carried out in the experimental section.