Improving diagnostic performance of a power transformer using an adaptive over-sampling method for imbalanced data

Dissolved gas analysis (DGA) of insulating oil in power transformers can offer valuable information related to faults. Due to the poor and unbalanced characteristics of typical DGA datasets, which threaten the generalization capability of artificial intelligent (AI)-based models, we propose the use of a new over-sampling technique called ASMOTE (adaptive synthetic minority over-sampling TEchnique) in the pre-processing step to enrich the dataset. ASMOTE can significantly improve the generalization performance of AI-based models by providing a sufficient synthetic dataset to train an AI classifier. To authenticate the effectiveness of the ASMOTE algorithm, we validate the transformer diagnostic accuracy of some typical classification algorithms such as multilayer perceptron (MLP), support vector machine (SVM), and k-nearest neighbor (k-NN) using synthetic datasets created by the SMOTE technique. In addition, the use of DGA ratios is also considered. By investigating the interactions between byproduct gases in insulating oil and transformer faults, the non-code ratios of the dissolved emissions are chosen as the characterizing input to the AI-based models. Moreover, with the ability to extract discriminate faulty information of a transformer from DGA data, MLP is used as a preferable classifier for diagnosing symptoms present in transformers. The empirical results of this study demonstrate that the proposed technique remarkably increases the diagnostic performance of power transformer faults.