A Comparative Study of Machine Learning Classifiers in Oil-Immersed Power Transformer Fault Diagnosis
Main Article Content
Abstract
The most common fault diagnosis method for oil-immersed power transformers is dissolved gas analysis (DGA). Doernenburg ratios, Rogers ratios, IEC ratios, and Duval's triangle are conventional DGA techniques for insulating oil in power transformers. In this study, Scikit-learn known as a popular open-source free machine learning tool for Python programming language has been used to develop different machine learning (ML) classifiers to effectively detect defects in oil-immersed power transformers. These classifiers are Decision Trees, Support Vector Machines, Gaussian Naive Bayes, K-Nearest Neighbours, Random Forests, and Multi-Layer Perceptron. The input vector of each classifier has been formed by Doernenburg ratios, Rogers ratios, IEC ratios, and CSUS. After these classifiers are completely trained, unseen DGA data sets are then used to evaluate their performances. Based on a statistical analysis, the study can indicate the most efffective type of the input vector and ML classfier for precisely detecting faults in power transformers.
Keywords
Power transformers, dissolved gas analysis, fault diagnosis, machine learning
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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and rogers methods for transformer fault diagnosis based on dissolved gas analysis using Matlab Simulink
Tools. 2015 IEEE Conference on Energy Conversion
(CENCON), 2015, pp. 363-367.
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computational intelligence and conventional dissolved
gas analysis methods for transformer fault diagnosis.
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25, no. 5, pp. 1798-1806, Oct.2018.
https://doi.org/10.1109/TDEI.2018.007191
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A comparative study of neural network efficiency in
power transformers diagnosis using dissolved gas
analysis. IEEE Transactions on Power Delivery, vol.
16, no. 4, pp. 643-647, Oct. 2001.
https://doi.org/10.1109/61.956751
[5] Jiejie Dai, Hui Song, Gehao Sheng, Xiuchen Jiang.
Dissolved gas analysis of insulating oil for power
transformer fault diagnosis with deep belief network.
IEEE Trans. Dielectrics and Electrical Insulation, vol.
24, no. 5, pp. 2828-2835, Oct. 2017.
https://doi.org/10.1109/TDEI.2017.006727
[6] Q. Su, C. Mi, L. L. Lai, P. Austin. A fuzzy dissolved
gas analysis method for the diagnosis of multiple
incipient faults in a transformer. IEEE Trans. Power
Systems, vol. 15, no. 2, pp. 593 - 598, May. 2000.
https://doi.org/10.1109/59.867146
[7] Secil Genc, Serap Karagol. Fuzzy Logic Application
in DGA Methods to Classify Fault Type in Power
Transformer. 2020 International Congress on HumanComputer Interaction, Optimization and Robotic
Applications (HORA), 2020.
https://doi.org/10.1109/HORA49412.2020.9152896
[8] Jinzhong Li, Qiaogen Zhang, Ke Wang, Jianyi Wang,
Tianchun Zhou, Yiyi Zhang. Optimal dissolved gas
ratios selected by genetic algorithm for power
transformer fault diagnosis based on support vector
machine. IEEE Trans. Dielectrics and Electrical
Insulation, vol. 23, pp. 1198-1206, May. 2016.
https://doi.org/10.1109/TDEI.2015.005277
[9] Qiang Liu, Guoqiang Huang, Chen Mao, Yu Shang,
Fan Wang. Recognition of dissolved gas in transformer
oil by ant colony optimization support vector machine.
2016 IEEE International Conference on High Voltage
Engineering and Application (ICHVE), 2016.
https://doi.org/10.1109/ICHVE.2016.7800837
[10] Arief Basuki, Suwarno. Online Dissolved Gas
Analysis of Power Transformers Based on Decision
Tree Model. 2018 Conference on Power Engineering
and Renewable Energy (ICPERE), 2018.
https://doi.org/10.1109/ICPERE.2018.8739761
[11] Y. Benmahamed, Y. Kemari, M. Teguar,
A. Boubakeur. Diagnosis of Power Transformer Oil
Using KNN and Nave Bayes Classifiers. 2018 IEEE
2nd International Conference on Dielectrics (ICD),
2018.
https://doi.org/10.1109/ICD.2018.8514789
[12] Ibrahim, Saleh I., Sherif S. M. Ghoneim, Ibrahim B.
M. Taha.DGALab: An Extensible Software
Implementation for DGA. IET Generation,
Transmission & Distribution 12, no. 18, pp. 4117-
4124, Oct. 2018.
https://doi.org/10.1049/iet-gtd.2018.5564
[13] Son T. Nguyen, Stefan Goetz, Dissolved Gas Analysis
of Insulating Oil for Power Transformer Fault
Diagnosis with Bayesian Neural Network, Journal of
Science and Technology: Smart Systems and Devices,
Vietnam Technical Universities, vol 32, no. 3, pp. 61 -
68, Sep. 2022.
https://doi.org/10.51316/jst.160.ssad.2022.32.3.8