New Dissolved Gas Analysis Diagnostics Framework for Substation Transformers using Random Forest Algorithm and IEEE C57.104 – 2019TM Guide

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The framework achieved 100% accuracy in diagnosing transformers using the random forest algorithm.
Compared to traditional methods, the random forest model outperformed the other algorithms with a 89.14% cross-validation score.
A total of 277 datasets from various Distribution Utilities were analyzed to validate the framework's effectiveness.
Machine learning methods like ANN and SVM showed lower accuracies, indicating limitations of traditional diagnostics.

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This framework predicts transformer health status using dissolved gas analysis, highlighting machine learning's role in enhancing diagnostics.

Tan et al. (2025) studied this question.

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This framework predicts transformer health status using dissolved gas analysis, highlighting machine learning's role in enhancing diagnostics.