Open Access Open Access  Restricted Access Subscription or Fee Access

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

(*) Corresponding author

Authors' affiliations



The availability of power transformers is essential for the safety and continuity of electrical service. Today's fault diagnosis methods use intelligent techniques such as neural networks, support machines, hybrid techniques, among others. Although they present good results, these techniques find restrictions in the ability to determine the precise moment in the event of multiple and small-magnitude faults. The proposal includes a new algorithm based on fuzzy rules that incorporates the daily increase of dissolved gases in the transformer oil that improves the classification of incipient faults. With reliable samples of gas dissolved in oil, the method proposed in the research can obtain a total precision rate of 91.4%. In contrast, this degree of precision is lower in other conventional methods reported in the bibliography. In addition, its performance in the classification of multiple failures is 97.5%. The method uses fuzzy logic tools to suggest actions aimed at preventive maintenance by monitoring the total of combustible gases dissolved in the oil. The proposal is a simple and easy solution to implement in practice that allows determining the status of the transformer in service without affecting the continuity of the electricity supply.
Copyright © 2022 Praise Worthy Prize - All rights reserved.


Fault Diagnosis; Fuzzy Logic; Dissolved Gas Analysis; Power Transformer

Full Text:



A. Naderian, S. Cress, R. Piercy, F. Wang, J. Service, An approach to determine the health index of power transformers, Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, 2008, pp. 192-196.

A. A. Etumi, F. Anayi, The application of correlation technique in detecting internal and external faults in three-phase transformer and saturation of current transformer, IEEE Transactions on Power Delivery, vol. 31, n. 5, pp. 2131-2169, 2016.

T. Committee, IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104™. 2019.

M. Bagheri, A. Zollanvari, S. Nezhivenko, Transformer Fault Condition Prognosis Using Vibration Signals Over Cloud Environment, in IEEE Access, vol. 6, pp. 9862-9874, 2018.

A. Alzghoul, B. Backe, M. Löfstrand, A. Byström, B. Liljedahl, Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application, Computers in industry, vol. 65, n. 8, pp. 1126-1135, 2014.

S. A. Wani, S.A. Khan, G. Prasha, D. Gupta, Smart diagnosis of incipient faults using dissolved gas analysis-based fault interpretation matrix (FIM), Arabian Journal for Science and Engineering, vol. 44, n. 8, pp. 6977-6985, 2019.

Y. Yahya, A. Qian, A. Yahya, Power transformer fault diagnosis using fuzzy reasoning spiking neural P systems, Journal of Intelligent Learning Systems and Applications, vol. 8, n. 4, pp. 77-91, 2016.

K. Chatterjee, S. Dawn, V. K. Jadoun, R. Jarial, Novel prediction-reliability based graphical DGA technique using multi-layer perceptron network & gas ratio combination algorithm, IET Science, Measurement & Technology, vol. 13, n. 6, pp. 836-842, 2019.

H. MehdipourPicha, R. Bo, H. Chen, M. M. Rana, J. Huang, F. Hu, Transformer Fault Diagnosis Using Deep Neural Network, 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2019, pp. 4241-4245.

M. Ou, H. Wei, Y. Zhang, J. Tan, A dynamic adam based deep neural network for fault diagnosis of oil-immersed power transformers, Energies, vol. 12, n. 6, pp. 995, 2019.

J. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou, Y. Zhang, Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, n. 2, pp. 1198-2006, 2012.

W. Mo, T. Kari, H. Wang, L. Luan, W. Gao, Power Transformer Fault Diagnosis Using Support Vector Machine and Particle Swarm Optimization, 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, pp. 511-515.

Y. Lu, C. Wei, T. Kong, T. Shi, J. Zheng, An improved DAG-SVM algorithm based on KFCM in Power Transformer Fault Diagnosis, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019, pp. 1297-1302.

Matar, M., Mohamed, O., Fault Classification on a Power Transmission Line Using Discrete Wavelet Transform and Artificial Neural Networks, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 349-357.

J. R. Nuñez et al., Design of a Fuzzy Controller for a Hybrid Generation System, IOP Conf. Series: Materials Science and Engineering, vol. 844, pp. 012017. 2020.

A. Hoballah, D. -E. A. Mansour, I. B. M. Taha, Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements, in IEEE Access, vol. 8, pp. 139176-139187, 2020.

A. I. Koldaev, A. A. Evdokimov, B. M. Shebzukhova, An Approach to Neuro-Fuzzy Monitoring of Power Transformers, 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), 2020, pp. 1-5.

I. B. Taha, A. Hoballah, S. S. Ghoneim, Optimal ratio limits of rogers' four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 27, n. 1, pp. 222-230, 2020.

L. Tightiz, M. A. Nasab, H. Yang, A. Addeh, An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis, ISA Transactions, vol. 103, pp. 63-74, 2020.

Moloi, K., Jordaan, J., Hamam, Y., The Development of a High Impedance Fault Diagnostic Scheme on Power Distribution Network, (2020) International Review of Electrical Engineering (IREE), 15 (1), pp. 69-79.

C. Guo, B. Wang, Z. Wu, M. Ren, Y. He, R. Albarracín, M. Dong, Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning, IET Generation, Transmission & Distribution, vol. 14, n. 11, pp. 2202-2208, 2020.

H. Malik, R. Sharma, S. Mishra, Fuzzy reinforcement learning based intelligent classifier for power transformer faults, ISA Transactions, vol. 101, pp. 390-398, 2020.

R. A. Prasojo, H. Gumilang, N. U. Maulidevi, B. A. Soedjarno, A Fuzzy Logic Model for Power Transformer Faults Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation, Energies, vol. 13, n. 4, pp.1009, 2020.

X. Wang, F. Guo, W. Xu, DGA fuzzy logic diagnostic method based on subordinating function, 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 2020, pp. 1381-1384.

M. Žarković, Z. Stojković, Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics, Electric Power Systems Research, vol. 149, pp. 125-136, 2017.

M. M. A. S. Mahmoud, Z. Qurbanov, Review of Fuzzy and ANN Fault Location Methods for Distribution Power System in Oil and Gas Sectors, IFAC-PapersOnLine, vol. 51, n. 30, pp. 263-267, 2018.

S. A. Wani, D. Gupta, M. U. Farooque, S. A. Khan, Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA) IET Science, Measurement & Technology, vol. 13, n. 7, pp. 959-967, 2019.

I. Marriaga-Márquez, K. Gómez-Sandoval, J. W. Grimaldo-Guerrero, J. Nuñez-Álvarez, Identification of critical variables in conventional transformers in distribution networks, IOP Conference Series: Materials Science and Engineering, vol. 844, pp. 012009, 2020.

S. Apte, R. Somalwar, A. Wajirabadkar, Incipient Fault Diagnosis of Transformer by DGA Using Fuzzy Logic, 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2018, pp. 1-5.

W. Teng, S. Fan, Z. Gong, W. Jiang, M. Gong, Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network, Systems Science & Control Engineering, vol. 6, n. 3, pp. 359-363, 2018.

R. L. Z. Pacori, J. H. A. Alcántara, Identification of Internal Failure in Power Transformers Using Fuzzy Logic Through the Dissolved Gas Analysis in Mineral Insulating Oil, 2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON), 2020, pp. 1-4.

R. Palke, P. Korde, Dissolved Gas Analysis (DGA) to Diagnose the Internal Faults of Power Transformer by using Fuzzy Logic Method, 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 1050-1053.

M. Idrees et al., Fuzzy Logic Based Calculation and Analysis of Health Index for Power Transformer Installed in Grid Stations, 2019 International Symposium on Recent Advances in Electrical Engineering (RAEE), 2019, pp. 1-6.

F. Mohamad, K. Hosny, T. Barakat, Incipient Fault Detection of Electric Power Transformers Using Fuzzy Logic Based on Roger's and IEC Method, 2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019, pp. 303-309.

M. Duval, L. Lamarre. The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers, IEEE Electrical Insulation Magazine, vol. 30, n. 6, pp. 9-12, 2014.

N. Pattanadech, W. Wattakapaiboon, Application of Duval Pentagon Compared with Other DGA Interpretation Techniques: Case Studies for Actual Transformer Inspections Including Experience from Power Plants in Thailand, 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019, pp. 1-4.

M. Duval, A. DePabla, Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases, IEEE Electrical Insulation Magazine, vol. 17, n. 2, pp. 31-41, 2001.

E. Li, L. Wang, B. Song. Fault diagnosis of power transformers with membership degree, IEEE Access, vol. 7, pp. 28791-28798, 2019.

J. Faiz, M. Soleimani, Dissolved gas analysis evaluation in electric power transformers using conventional methods a review, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, n. 2, pp. 1239-1248, 2017.

Adnan, N., Srivastava, V., Loss Reduction Concept Review and Its Comparison for Various Transmission Lines, (2019) International Review of Electrical Engineering (IREE), 14 (4), pp. 263-271.

I. A. Marriaga-Márquez, et al., Identification of critical variables in conventional transformers in distribution networks, IOP Conf. Series: Materials Science and Engineering, vol. 844, pp. 012009, 2020.

S. Díaz, J. Nuñez, K. Berdugo, K. Gomez, Study of technologies implemented in the operation of SF6 switches, IOP Conf. Series: Materials Science and Engineering, vol. 72, pp. 012041, 2020.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize