Abstract

This research presents the study and analysis of faults in the oil-immersed transformer. Abnormalities can be analyzed from the Dissolved Gas Analysis (DGA) test. The oil has a variety of compounds such as hydrocarbons, oxygen, nitrogen and hydrogen. The amount of gas in oil has distinctive characteristics that can indicate abnormalities in different types. This program is developed based on according to standard IEEE Std C57.104TM - 2019. This program is designed to detect for defects in oil-immersed transformers by using artificial neural networks (Artificial Neural Network: ANN). The program using MATLAB programs is prepared for maintenance planning and supports applications from basic learning to industrial sector.

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