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

TL;DR

This paper compares traditional machine learning and deep learning models for fault detection in power transformers, finding that both approaches perform similarly with high accuracy, aiding in electrical system safety.

Contribution

It provides a comprehensive benchmarking of ML and DL models for transformer fault classification using real-world data, highlighting their comparable effectiveness.

Findings

Random Forest achieved 86.82% accuracy

1D-CNN achieved 86.30% accuracy

Both approaches performed comparably in fault detection

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%.