Distribution transformers play a vital role in power distribution systems, and their reliable operation is crucial for grid stability. This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers, integrating Sample Ensemble Learning (SEL) with a Self-Optimizing Support Vector Machine (SO-SVM). The SEL technique enhances data diversity and mitigates class imbalance, while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy. A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios, including inter-turn winding faults, core saturation, and thermal aging. Feature vectors were extracted from voltage, current, and temperature measurements to train and validate the proposed hybrid model. Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%, a precision of 96.5%, and an F1-score of 97.2%. Beyond classification, the model effectively identified incipient faults, providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters. This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions. The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments, offering a promising foundation for future real-time and field-level implementations.
Yu et al. (Thu,) studied this question.