Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers

• URL: http://arxiv.org/abs/2505.06295v1
• Date: Wed, 07 May 2025 15:19:53 GMT
• Title: Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers
• Authors: Bhuvan Saravanan, Pasanth Kumar M D, Aarnesh Vengateson,
• Abstract summary: This study presents a comparative analysis of conventional machine learning (ML) algorithms and deep learning (DL) algorithms for fault classification of power transformers.<n>Using a condition-monitored dataset spanning 10 months, various gas concentration features were normalized and used to train five ML classifiers.<n>The RF model achieved the highest ML accuracy at 86.82%, while the 1D-CNN model attained a close 86.30%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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