Online Dissolved Gas Analysis of Power Transformers Based on Decision Tree Model

摘要

This paper presents the possibility of using one of machine learning model, decision tree with C4.5 algorithm for gas interpretation in online condition monitoring and diagnostic application of power transformers. Decision tree selection is based on the best learning outcomes of machine learning software (WEKA and Orange) compared to naïve Bayes, neural network, nearest neighbour and support vector machine models. The decision tree was built from 715 data, 7 attributes of gas and 9 types of fault which were cleaned by interquartile range method become 471 data. Evaluation result based on correction prediction are 95.54% using data training, 88.32% using cross validation and 87.23% using 10% random data from data training. The decision tree rule was implemented in online condition monitoring and diagnostic of power transformer which is integrated into SCADA system. This implementation result can predict transformers fault from gas values by online better than conventional DGA methods.