Rancang Bangun Aplikasi Sistem Diagnosis Transformator untuk Memprediksi Waktu Purifikasi dan Umur Transformator Berbasis Machine Learning

Abstract

Transformers are equipment in high voltage engineering that often experience abnormalities originating from internal or external sources. This abnormality causes disturbances, so it is necessary to diagnose faults on the transformer as early as possible so that fault predictions and maintenance actions can be carried out to maintain the quality of work on the transformer. To help with diagnosis, an application was designed that can diagnose the condition of the transformer, predict purification time and transformer life to maintain the quality of the transformer's work. This application is designed to help make it easier to predict transformer conditions using dissolved gas analysis test results, oil scores, furran and tap changer. The predicted data is data for the GT 2.1 Excitation transformer, GT 2.1 Main Transformer, and GT 2.1 UAT transformer. Based on the application design, the test results were 100% successful, from the 10 tests carried out all the tests were validated by the system. The prediction results that have been tested on the application result in the GT 2.1 Transformer having interference based on the key gas method and the CO2/CO ratio method, the transformer oil purification time is predicted to be 5679 days since the last test and the predicted life time of the transformer is more than 15 years. Transformer GT 2.1 Main Transformer is predicted to have problems based on the key gas method and the CO2/CO ratio method, the transformer oil purification time is predicted to be 2697 days since the last test and the predicted life time of the transformer is more than 15 years. The GT 2.1 UAT transformer is predicted to experience problems based on the Rogers and Key Gas methods, the transformer oil purification time is predicted to be 1866 days since the last test and the transformer life time is predicted to be more than 15 years