Citation :

ApolinarioAwit, Joseph Jay Brañanola, Maria Vina Presbitero, Edizar Sablas, Rannel Sinangote, John Kenneth Tabla, Jestoni P. Tan, Emerita M. Tan, Chona R. Dagatan, "New Diagnostic Framework for Transformer Health using IEEE C57.104 2019 with Gradient Boosting and PCA of DGA Data," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 244-260, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P118

Abstract

Transformers play an indispensable role in any power system. The health condition of these devices should be the top priority. Early fault detection of these devices is essential to have sustainable power flow. There are many routinary transformer tests like winding test, furan analysis, insulation resistance tests, but dissolved gas analysis stands to be one of the most critical tests among others. This is to the fact that the DGA test can evaluate the major condition of the transformer. Dissolved Gas Analysis (DGA) methods, while widely used, often struggle with accuracy and scalability under complex fault scenarios. This paper proposed a novel ML-based DGA framework that integrates the IEEE standard with Principal Component Analysis (PCA) and Gradient Boosting Machine (GBM) to enhance transformer fault diagnosis. PCA captures 95% of the variance with five principal components. The framework showed a test accuracy of 87.5% and a cross-validation accuracy of 86.05%, outperforming traditional methods such as the Duval Triangle (83.08%) and IEC Ratio Method (82.05%), as well as other machine learning models, including Random Forest (77%) and Support Vector Machines (37%). These findings demonstrate the effectiveness of the framework as a soft sensor in Transformer diagnostics.

Keywords

Dissolved Gas Analysis, Gradient Boosting Machine, Transformer Health, Principal Component Analysis.

References

[1] M. Duval, “A Review of Faults Detectable by Gas-In-Oil Analysis in Transformers,” IEEE Electrical Insulation Magazine, vol. 18, no. 3, pp. 8-17, 2002.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Lefeng Cheng, and Tao Yu, “Dissolved Gas Analysis Principle-based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey,” Energies, vol. 11, no. 4, pp. 1-69, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Zhiyuan He et al., “Gradient Boosting Machine: A Survey,” arXiv preprint, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Yikuan Li et al., “BEHRT: Transformer for Electronic Health Records,” Scientific Reports, vol.  10, no. 1, pp. 1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Shufali Ashraf Wani et al., “Advances in DGA based Condition Monitoring of Transformers: A Review,” Renewable and Sustainable Energy Reviews, vol. 149, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Matias Meira et al., “Power Transformers Monitoring based on Electrical Measurements: State of the Art,” IET Generation, Transmission and Distribution, vol. 12, no. 12, pp. 2805-2815, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[7] C57.104-2019 - IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, Institute of Electrical and Electronics Engineers, pp. 1-98, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8890040

[8] Shubham Dadaso Patil et al., “DGA based Ensemble Learning Approach for Power Transformer Fault Diagnosis,” 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), Bhubaneswar, India, pp. 722-727, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Rohan Raghuraman, and Atena Darvishi, “Detecting Transformer Fault Types from Dissolved Gas Analysis Data using Machine Learning Techniques,” 2022 IEEE 15^th Dallas Circuit and System Conference (DCAS), Dallas, TX, USA, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Arnaud Nanfak et al., “Interpreting Dissolved Gasses in Transformer Oil: A New Method based on the Analysis of Labelled Fault Data,” IET Generation, Transmission and Distribution, vol. 15, no. 21, pp. 3032-3047, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Akanksh Basavaraju et al., “A Machine Learning Approach to Road Surface Anomaly Assessment using  Smartphone Sensors,” IEEE Sensors Journal, vol. 20, no. 5, pp. 2635-2647, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[12] David John Gagne et al., “Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms,” Monthly Weather Review, vol. 147, no. 8, pp. 2827-2845, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Yeonuk Kim et al., “Gap‐Filling Approaches for Eddy Covariance Methane Fluxes: A Comparison of Three Machine Learning Algorithms and a Traditional Method with Principal Component Analysis,” Global Change Biology, vol. 26, no. 3, pp. 1499-1518, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Md Nazmul Islam et al., “Vision Transformer and Explainable Transfer Learning Models for Auto Detection of Kidney Cyst, Stone and Tumor from CT-Radiography,” Scientific Reports, vol. 12, no. 1, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Amir Esmaeili Nezhad, and Mohammad Hamed Samimi, “A Review of the Applications of Machine Learning in the Condition Monitoring of Transformers,” Energy Systems, vol. 15, no. 1, pp. 463-493, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Miodrag Zivkovic et al., “Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID- 19 Early Diagnostics from X-Ray Images,” Electronics, vol. 11, no. 22, pp. 1-30, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Rolandos Alexandros Potamias, Georgios Siolas, and Andreas - Georgios Stafylopatis, “A Transformer-based Approach to Irony and Sarcasm Detection,” Neural Computing and Applications, vol. 32, no. 23, pp. 17309-17320, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Chengying Zhao et al., “A Double-Channel Hybrid Deep Neural Network based on CNN and BiLSTM for Remaining useful Life Prediction,” Sensors, vol. 20, no. 24, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Dongsheng Yang et al., “A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals with an Imbalanced Data Structure,” IEEE Transactions on Industrial Electronics, vol. 69, no. 2, pp. 1977-1987, 2022.
        [CrossRef] [Google Scholar] [Publisher Link]