Sep 2026· International Journal of Power Electronics and Drive Systems (IJPEDS)· 0 citations· 27 references
Abstract
Failures and guaranteed dependability of the electrical grid, early fault diagnosis in power transformers is essential. By examining gas ratios suggestive of faults, dissolved gas analysis (DGA) continues to be a vital component for transformer health monitoring. Using four preprocessing techniques raw data, min-max normalization, logarithmic transformation, and square root transformation; this study suggests a machine learning method for fault detection using DGA gas ratios (such as CH₄/H₂, C₂H₂/C₂H₄). Random forest (RF), support vector machines (SVM), gradient boosted trees (GBT), and a hybrid RF-GBT model that uses prediction fusion were the four supervised classifiers assessed. Performance was assessed using accuracy, precision, recall, f1-score, and Cohen's kappa. Experimental results show that the hybrid RF-GBT model with logarithmic transformation achieves the highest performance, with 94.93% accuracy and 92.37% Cohen's kappa, significantly outperforming individual classifiers. Data preprocessing, particularly logarithmic and square root transformations, enhances diagnostic robustness by mitigating feature skewness. This study underscores the importance of tailored preprocessing and ensemble methods for reliable transformer fault diagnosis.
This article explores machine learning techniques (MLTs) as a modern alternative to enhance the interpretation of DGA data for early-stage fault detection in service transformers, and demonstrates that random forest and gradient boosting outperform others, achieving up to 98% accuracy.
Rupali Balabantaraya, A. Chatterjee, A. Sahoo et al.· Electrica· 0 citations
Dissolved gas analysis is a widely used tool for the early detection of faults in power transformers. However, conventional interpretation methods, such as Rogers, Doernenburg, Key Gas method, and the Duval graphical methods, may yield different diagnoses for the same sample because of their distinct criteria, gas rati...
Helen J. Alarcon Carizales, A. R. Romero-Quete, S. Rivera-Rodríguez· IEEE Access· 0 citations
This study presents a novel diagnostic methodology for oil-filled transformers, leveraging dissolved gas analysis (DGA) and a no-code machine learning framework. Conventional diagnostic methods such as Duval Triangle and gas ratio techniques are constrained by limited adaptability to modern transformer designs, suscept...
C. Chan, Salmey Abdul Halim, Nursyarizal B. Mohd Nor et al.· Jurnal Kejuruteraan· 0 citations
Problems with power transformers reduce grid reliability and can lead to large financial losses. Traditional Dissolved Gas Analysis (DGA) methods, such as the Key Gas Method, Ratios, and Duval Triangle, often give unclear results when faults are complex or occur together. This study introduces a Statistically Guided De...
Bongumsa Mendu, O. Oni, O. M. Longe· Energies· 0 citations
To improve the quality of extracting characteristic parameters of dissolved gases in transformer oil and the accuracy of fault diagnosis, this paper proposes a new method for transformer fault diagnosis based on the Fisher criterion and the Black-winged Kite Algorithm (BKA) to optimize the Kernel Extreme Learning Machi...
Bo Yang, Ying Li, Yu-Peng Li· European Conference on Elect...· 0 citations
Power transformers are among the most critical components of electric power transmission and distribution systems, and unexpected failures can lead to substantial economic losses and prolonged power outages. Dissolved Gas Analysis (DGA) is the most widely used diagnostic technique for transformer fault diagnosis and in...
