Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers
This work addresses fault detection in power transformers for electrical power systems, but it is incremental as it applies existing methods to a new dataset.
This study compared traditional machine learning and deep learning models for fault detection in power transformers, finding that both approaches performed similarly with Random Forest achieving the highest accuracy at 86.82% and 1D-CNN at 86.30%.
Accurate diagnosis of power transformer faults is essential for ensuring the stability and safety of electrical power systems. This study presents a comparative analysis of conventional machine learning (ML) algorithms and deep learning (DL) algorithms for fault classification of power transformers. Using a condition-monitored dataset spanning 10 months, various gas concentration features were normalized and used to train five ML classifiers: Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), XGBoost, and Artificial Neural Network (ANN). In addition, four DL models were evaluated: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Network (1D-CNN), and TabNet. Experimental results show that both ML and DL approaches performed comparably. The RF model achieved the highest ML accuracy at 86.82%, while the 1D-CNN model attained a close 86.30%.