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

Bhuvan Saravanan, Pasanth Kumar M D, Aarnesh Vengateson
5/7/2025
cs.LG

No official code found. Best implementation: BhuvanSaravanan/power_transformer_fault_detection (4 stars, MIT, last commit 2025-07-22).

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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Code Implementations(1)

BhuvanSaravanan/power_transformer_fault_detectionCommunity100%
MIT
41Jupyter NotebookMay 7, 20251 years agoMIT

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Cite this paper

@article{saravanan2025benchmarking,
  title  = {Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers},
  author = {Bhuvan Saravanan and Pasanth Kumar M D and Aarnesh Vengateson},
  year   = {2025},
  eprint = {2505.06295},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.06295v1}
}

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