Research Article
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Predictive Model for Incipient Faults in Oil-Filled Transformers

Year 2024, , 302 - 313, 31.08.2024

Efosa Igodan , Michael Osajeh , Linda Usiosefe

https://doi.org/10.35377/saucis...1414115

Abstract

The power transformer is an invaluable piece of device in the power system. To prevent catastrophic failures and the ensuing power outages, the status of a transformer linked to a system must be examined for any possible faults. Despite using DGA as a global tool for detecting faults, it is limited by the inability to accurately solve the problem associated with results variability due to the intrinsic nature of the IEC TC 10 database. This study proposed a data-driven fault/defect diagnostic model using four ensemble models with three base classifiers respectively. The base classifiers are comprised of SVM, C4.5 decision tree, and naive Bayes while the ensemble methods are comprised of stacking, voting, boosting and bagging respectively. The DGA dataset used comprises seven features and 168 instances split into training (i.e. 56%) and test (i.e. 44%) datasets respectively. The results indicate that C4.5 obtained a 98.33% accuracy while stacking obtained a 99.89% accuracy as the best-performing base and ensemble models respectively. The high classification performance accuracy achieved by our proposed models indicates its capacity for real-world applications. It can be applied to advance automation in mobile-based technology.

Keywords

Dissolve gas analysis power transformer Naive Bayes support vector machine C4.5 ensemble learning

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography

Supporting Institution

Non

References

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Year 2024, , 302 - 313, 31.08.2024

Efosa Igodan , Michael Osajeh , Linda Usiosefe

https://doi.org/10.35377/saucis...1414115

Abstract

References

  • [1] Duval M, dePablo A (2001) Interpretation of Gas-In-Oil Analysis Using New IEC Publication 60599 and IEC TC 10 Databases. IEEE Electrical Insulation Magazine, vol. 17, no. 2, pp. 31–41.
  • [2] Izzularab MA, Aly, GEM, Mansour DA (2004) On-line diagnosis of incipient faults and cellulose degradation based on artificial intelligence methods”, IEEE Int. Conf. on Solid Dielectrics, Toulouse, France.
  • [3] Hoballah, A., Mansour, J. G. and Taha IBM, (2020) Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements. IEEE Access, vol. 8, pp. 139176–139187.
  • [4] Mahamdi Y, Boubakeur A, Mekhaldi A, Benmahamed Y (2022) Power Transformer Fault Prediction using Naive Bayes and Decision tree based on Dissolved Gas Analysis. ENP Engineering Science Journal, Vol. 2, No. 1. Digital Object Identifier (DOI): 10.53907/enpesj.v2i1.63.
  • [5] Cheng L, Yu T (2018) Dissolved gas analysis principle-based intelligent approaches to fault diagnosis and decision making for large oil-immersed power transformers: A survey. Energies 11, 913. doi:10.3390/en11040913
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  • [35] Demirci M, Gozde H, Taplamacioglu MC (2021) Fault Diagnosis of Power Transformers with Machine Learning Methods using Traditional Methods Data. International Journal on Technical and Physical Problems of Engineering. Issue 49, Volume 13, Number 4, pp. 225-230
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  • [37] Ma H, Zhang W, Wu R, Yang C (2018) A Power transformers fault diagnosis model based on three DGA ratios and PSO optimization SVM. IOP Conf. Series: Materials Science and Engineering 339 012001. doi:10.1088/1757-899X/339/1/012001.
  • [38] Taha IBM, Mansour DEA (2021) Novel Power Transformer Fault Diagnosis Using Optimized Machine Learning Methods. Intelligent Automation & Soft Computing. IASC, 2021, vol.28, no.3. DOI:10.32604/iasc.2021.017703
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There are 61 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Efosa Igodan 0000-0003-2102-3597

Michael Osajeh 0009-0000-8917-9446

Linda Usiosefe 0009-0000-7425-4204

Early Pub Date August 29, 2024
Publication Date August 31, 2024
Submission Date January 3, 2024
Acceptance Date July 1, 2024
Published in Issue Year 2024

Cite

IEEE E. Igodan, M. Osajeh, and L. Usiosefe, “Predictive Model for Incipient Faults in Oil-Filled Transformers”, SAUCIS, vol. 7, no. 2, pp. 302–313, 2024, doi: 10.35377/saucis...1414115.

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