A feature selection and ensemble learning based methodology for transformer fault diagnosis

Abstract

Dissolved gas analysis (DGA) data are generally used to diagnose a transformer fault. However, the measurement errors in DGA data are inevitable and will affect the accuracy and reliability of the diagnosis results. Nevertheless, so far, only a few efforts have been devoted to addressing this issue. To provide an accurate and stable transformer fault diagnosis system, a feature selection and ensemble learning based methodology is proposed. Firstly, an Overlapping Information Feature Selection (OIFS) method is proposed to select efficient features from the given feature set for classifiers. Secondly, an Intelligent Voting Ensemble Learning (IVEL) method is proposed to generate a diagnosis model based on the feature combination from the OIFS method. The reported test results show that the OIFS method outperforms existing methods in 9 out of 10 tested classifiers. Additionally, the IVEL outperforms three popular ensemble learning methods, including random forest (RF), gradient boosting decision tree (GBDT), and LightGBM, in terms of both accuracy and robustness performances. Finally, the proposed methodology (OIFS-IVEL) is applied to diagnose the transformer faults in the IEC TC 10 database, achieving a 100% accuracy in recognizing fault types and a 92.6 % accuracy in evaluating fault severity.