Fundamental study on fault sign detection for oil immersed power transformer by dissolved gas analysis

Abstract

Dissolved gas analysis (DGA) is widely used as a method to diagnose internal abnormalities in electrical transformers, such as overheating and partial discharge. While electric power companies conduct inspections and repairs according to the diagnosis results based on DGA, more efficient maintenance work based on fault sign detection is required in terms of stable power supply and cost reduction. This paper shows the results of a basic study on the prediction of fault signs in oil-filled electrical transformers using DGA. In order to predict the fault signs in oil-filled transformers, the distance to the decision boundary and the classification probability generated by multiple machine learning methods were analyzed.