(untitled) (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
In the daily operation and maintenance of power systems, the condition monitoring of oil-immersed transformers has always been a significant issue. Dissolved Gas Analysis (DGA) is one of the effective methods for detecting faults in oil-immersed power transformers. However, in practical applications, factors such as the uneven distribution of faulty samples, inappropriate selection of feature dimensions, and difficulty in determining the parameters of the classifier limit the accuracy of traditional diagnostic methods, making the diagnostic results vulnerable to interference. This paper presents a hybrid fault diagnosis scheme that integrates the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling of minority class samples, feature importance selection based on LightGBM (Light Gradient Boosting Machine), and the eXtreme Gradient Boosting classifier (XGBoost) optimized by the Sparrow Search Algorithm. The experimental results on the public dataset show that the proposed method achieved an accuracy of 97.42%, a Macro-F1 score of 97.36%, and a Kappa coefficient of 0.9677. Compared to the unoptimized XGBoost, SSA optimization improved accuracy by 0.42% and Macro-F1 by 0.48%, This demonstrates that automatic hyperparameter tuning is effective. The proposed method also outperforms SVM (80.26%), Random Forest (93.56%), and PCA + SSA-LightGBM (88.41%). In addition, the ablation experiment further validated the role played by each component.