Accurate Identification of Transformer Faults From Dissolved Gas Data Using Recursive Feature Elimin

Dissolved gas analysis (DGA) of insulating oils is one of the most popular methods to detect incipient faults in power transformers. However, appropriate feature selection is crucial for accurately detecting incipient faults using DGA data. Another issue is the unavailability of a balanced DGA dataset, which can hamper the fault classification accuracy. Considering these two issues, this article proposes a novel and accurate fault classification framework using gas ratios as features obtained from the DGA data of power transformers. The obtained unbalanced DGA data was initially balanced using the synthetic minority oversampling technique (SMOTE) in the data pre-processing stage. Following this, an efficient feature selection algorithm, namely, recursive feature elimination (RFE) was used to select the best possible features prior to the fault classification using three benchmark machine learning (ML) classifiers, namely, k -nearest neighbor (KNN), multiclass support vector machines (SVMs), and extreme gradient boost (XGBoost). The proposed classification model was tested on the DGA data obtained from the local power utility and on the benchmark IEC TC-10 database. Investigations revealed that the proposed classification model delivered detection accuracy of 98.84% and 97.43%, respectively. The proposed method may be reliably used to diagnose incipient faults in power transformers.