Abstract
Power transformers are critical assets in electrical power systems, yet their fault diagnosis
often relies on conventional dissolved gas analysis (DGA) methods such as the Duval
Pentagon and Triangle, Key Gas, and Rogers Ratio methods. Even though these methods
are commonly used, they present limitations in classification accuracy, concurrent fault
identification, and manual sample handling. In this study, a framework of optimized
machine learning algorithms that integrates Chi-squared statistical feature selection with
Random Search hyperparameter optimization algorithms was developed to enhance transformer
fault classification accuracy using DGA data, thereby addressing the limitations of
conventional methods and improving diagnostic precision. Utilizing the R2024b MATLAB
Classification Learner App, five optimized machine learning algorithms were trained and
tested using 282 transformer oil samples with varying DGA gas concentrations obtained
from industrial transformers, the IEC TC10 database, and the literature. The optimized
and assessed models are Linear Discriminant, Naïve Bayes, Decision Trees, Support Vector
Machine, Neural Networks, k-Nearest Neighbor, and the Ensemble Algorithm. From the
proposed models, the best performing algorithm, Optimized k-Nearest Neighbor, achieved
an overall performance accuracy of 92.478%, followed by the Optimized Neural Network
at 89.823%. To assess their performance against the conventional methods, the same dataset
used for the optimized machine learning algorithms was used to evaluate the performance
of the Duval Triangle and Duval Pentagon methods using VAISALA DGA software version
1.1.0; the proposed models outperformed the conventional methods, which could only
achieve a classification accuracy of 35.757% and 30.818%, respectively. This study concludes
that the application of the proposed optimized machine learning algorithms can enhance
the classification accuracy of DGA-based faults in power transformers, supporting more
reliable diagnostics and proactive maintenance strategies.