Streamlining Fault Classification of Dissolved Gases in Transformer Using Data Synthesis and Dimension Reduction

This article addresses the challenge of limited data availability of dissolved gas analysis (DGA) in fault diagnosis of power transformers. While various classifiers have been employed for fault classification in the prior research, their accuracy depends heavily on balanced and synthetic training data. Although resampling techniques have been implemented to address that, their statistical validity remains unproven. Synthetic data generation is particularly crucial for imbalanced and scarce DGA fault samples. However, there is limited discussion on generating synthetic data for imbalanced datasets, which can negatively impact classifier training and machine learning (ML) models. This article introduces a novel approach for generating synthetic data to overcome the limited availability of fault data in power transformer diagnosis. The effectiveness of the generated data is ensured through ${z}$ -score normalization of the original gas concentration values. In addition, dimension reduction from 5-D to 2-D data is performed to simplify the data generation process. The proposed approach utilizes t-distributed stochastic neighbor embedding (t-SNE) as a superior dimension reduction technique, verified through reconstruction error and explained variance (Ev) metrics. The performance of the proposed generative adversarial network (GAN)-based approach is compared with four standard algorithms using multiple evaluation metrics. The results demonstrate that the GAN outperforms the other algorithms in generating synthetic data that closely resembles actual data. The proposed method achieves an accuracy of up to 98.10%, surpassing existing methods. This confirms the proposed GAN-based approach, in conjunction with t-SNE, effectively processes actual data for power transformer fault diagnosis, thus overcoming the limitations of limited data availability.