Interpretation of Gases Dissolved in Dielectric Oil Using Random Forests for the Detection of Anomalies in Power Transformers

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Armando Freire
https://orcid.org/0000-0002-2447-3369
Juan Astudillo
Carlos Quinatoa
https://orcid.org/0000-0001-6369-7480
Fernando Arias

Abstract

The following paper presents a machine learning tool for the interpretation of anomalies in power transformers using the random forest method. Using the results of gas chromatography tests on dielectric oil from several published papers, the data set delivered by the dissolved gas analysis (DGA) in quantities of parts per million (ppm), the amount of hydrocarbon gases such as hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) that serve to diagnose the internal state of the transformer is used. Due to the reduced number of collected data, there is a disadvantage to apply artificial neural networks, support vector machine, among others that need large amounts of data for each variable, but satisfactorily they are solved using random forests, because this methodology classifies better the data of smaller amount. The learning obtained by training is validated with the states obtained by the test data under IEC 60599 and IEEE C57-104, which encompass 4 diagnostics such as high energy discharge, low energy discharge, normal state and overheating, resulting in a final corroborative validation criterion for the algorithm by comparing the diagnostic results with the random forests.

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How to Cite
Freire, A., Astudillo, J., Quinatoa, C., & Arias, F. (2023). Interpretation of Gases Dissolved in Dielectric Oil Using Random Forests for the Detection of Anomalies in Power Transformers. Revista Técnica "energía", 19(2), PP. 90–98. https://doi.org/10.37116/revistaenergia.v19.n2.2023.544
Section
TECNOLÓGICOS E INNOVACIÓN

References

A. Naderian, S. Cress, R. Piercy, F. Wang, and J. Service, “An approach to determine the health index of power transformers,” Conf. Rec. IEEE Int. Symp. Electr. Insul., pp. 192–196, 2008, doi: 10.1109/ELINSL.2008.4570308.

A. D. Ashkezari, H. Ma, T. Saha, and C. Ekanayake, “Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers,” IEEE Trans. Dielectr. Electr. Insul., vol. 20, no. 3, pp. 965–973, 2013, doi: 10.1109/TDEI.2013.6518966.

G. Lv, H. Cheng, H. Zhai, and L. Dong, “Fault diagnosis of power transformer based on multi-layer SVM classifier,” Electr. Power Syst. Res., vol. 75, no. 1, pp. 9–15, 2005, doi: 10.1016/j.epsr.2004.07.013.

D. V. S. S. S. Sarma and G. N. S. Kalyani, “ANN approach for condition monitoring of power transformers using DGA,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. C, pp. 444–447, 2004, doi: 10.1109/tencon.2004.1414803.

C. P. Hung and M. H. Wang, “Diagnosis of incipient faults in power transformers using CMAC neural network approach,” Electr. Power Syst. Res., vol. 71, no. 3, pp. 235–244, 2004, doi: 10.1016/j.epsr.2004.01.019.

K. Shrivastava and A. Choubey, “A novel association rule mining with IEC ratio based dissolved gas analysis for fault diagnosis of power transformers,” Int. J. Adv. Comput. Res., vol. 2, no. 2, 2012.

S. S. M. Ghoneim and I. B. Taha, “Artificial Neural Networks for Power Transformers Fault Diagnosis Based on IEC Code Using Dissolved Gas Analysis,” Int. J. Control. Autom. Syst., vol. 4, no. 2, pp. 18–21, 2015.

S. Chakravorti, D. Dey, and B. Chatterjee, Recent Trends in the Condition Monitoring of Transformers: Theory, Implementation and Analysis, vol. 67. 2013. doi: 10.1007/978-1-4471-5550-8.

M. Duval and A. DePablo, “Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases,” IEEE Electr. Insul. Mag., vol. 17, no. 2, pp. 31–41, 2001, doi: 10.1109/57.917529.

U. Djillali, L. D. E. Sidi, B. E. L. Abbes, S. Mohammed, and E. Amine, “Contributions des techniques intelligentes au diagnostic industriel des transformateurs de puissance,” 2019.

IEC 60599, Mineral oil-filled electrical equipment in service –Guidance on the interpretation of dissolved and free gases analysis, 3rd ed. 2015.

IEEE Std C57.104, IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, vol. 1991, no. 3. 1992. [Online]. Available: http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=29023%5Cnhttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:IEEE+Guide+for+the+Interpretation+of+Gases+Generated+in+Oil-Immersed+Transformers#0

B. Vahidi and A. Teymouri, Quality Confirmation Tests for Power Transformer Insulation Systems. 2019. doi: 10.1007/978-3-030-19693-6.

I. Vasilev, D. Slater, G. Spacagna, P. Roelants, and V. Zocca, Python Deep Learning. 2019.

L. Igual and S. Seguí, Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. 2017.

A. Prinzie and D. Van Den Poel, “Random multiclass classification: generalizing random forests to random MNL and random NB,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4653 LNCS, pp. 349–358, 2007, doi: 10.1007/978-3-540-74469-6_35.

A. Pajankar and A. Joshi, Introduction to Machine Learning with Scikit-learn. 2022. doi: 10.1007/978-1-4842-7921-2_5.

L. E. O. Breiman, “Random Forests,” pp. 5–32, 2001.

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