Transformer DGA Fault Diagnosis Based on SMOTEand SSA-Optimized XGBoost (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
In the daily operation and maintenance of power systems, thecondition monitoring of oil-immersed transformers has always been asignificant issue. Dissolved Gas Analysis (DGA) is one of the effectivemethods for detecting faults in oil-immersed power transformers. However, inpractical applications, factors such as the uneven distribution of faulty samples,inappropriate selection of feature dimensions, and difficulty in determining theparameters of the classifier limit the accuracy of traditional diagnostic methods,making the diagnostic results vulnerable to interference. This paper presents ahybrid fault diagnosis scheme that integrates the Synthetic MinorityOver-sampling Technique (SMOTE) for oversampling of minority classsamples, feature importance selection based on LightGBM (Light GradientBoosting Machine), and the eXtreme Gradient Boosting classifier (XGBoost)optimized by the Sparrow Search Algorithm. The experimental results on thepublic 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 tothe unoptimized XGBoost, SSA optimization improved accuracy by 0.42% andMacro-F1 by 0.48%, This demonstrates that automatic hyperparameter tuningis effective. The proposed method also outperforms SVM (80.26%), RandomForest (93.56%), and PCA + SSA-LightGBM (88.41%). In addition, theablation experiment further validated the role played by each component.