Transformer Fault Diagnosis Method Based on DC-SMOTE-SSA-SVM

This paper proposes a transformer fault diagnosis method based on DC-SMOTE-SSA-SVM, aimed at improving the accuracy of transformer fault diagnosis in power systems. The paper first highlights the importance of power transformers and the challenges associated with fault diagnosis, particularly in cases of imbalanced data samples. To address this issue, the DC-SMOTE algorithm is employed to augment fault samples derived from dissolved gases in oil, thereby enhancing the classification accuracy of the model. Subsequently, a fault diagnosis model is developed by integrating the global optimization capability of the Sparrow Search Algorithm (SSA) with the classification ability of Support Vector Machine (SVM). Through case studies, the effectiveness of the proposed method in improving fault diagnosis accuracy is validated. Experimental results demonstrate that, compared to the original dataset and the dataset processed with SMOTE, the DC-SMOTE-SSA-SVM model significantly improves both accuracy and AUC, exhibiting strong overall performance and high reliability, thus meeting the practical requirements for operation and maintenance.