Intelligent Fault Detection In a 25 MVA Transformer Using ANFIS

Azriyenni Zakri; Hari Firdaus; Wahri Sunanda; Boy Ihsan; Jafaru Usman

doi:10.33019/39mt0605

Authors

Azriyenni Zakri Department of Electrical Engineering, Faculty of Engineering, Universitas Riau
Hari Firdaus Department of Electrical Engineering, Faculty of Engineering, Universitas Riau
Wahri Sunanda Department of Electrical Engineering, Universitas Bangka Belitung https://orcid.org/0000-0003-4505-3491 (unauthenticated)
Boy Ihsan Department of Electrical Engineering, Faculty of Engineering, Universitas Riau
Jafaru Usman Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Maiduguri

DOI:

https://doi.org/10.33019/39mt0605

Keywords:

Adaptive Neuro-Fuzzy Inference System (ANFIS), Digital Relay Protection, Fault Classification, Power Transformer

Abstract

This study aims to develop an intelligent fault prediction model for a 25 MVA power transformer using the Adaptive Neuro-Fuzzy Inference System (ANFIS), to improve classification accuracy and ensure selective, reliable protection decisions in power systems. The research is grounded in the limitations of traditional differential relay protection, which struggles to distinguish between internal and external faults during transient conditions. ANFIS combines fuzzy logic’s ability to handle uncertainty with the adaptive learning of neural networks, making it a suitable tool for non-linear fault data analysis. A simulation was carried out in MATLAB/Simulink. Current signals from CTs on both primary and secondary sides served as input features. The model was trained using 270 data samples and tested with 30 samples. Two membership functions generalized bell-shaped (Gbell) and triangular (Tri) were evaluated. RMSE was used as the performance metric. The ANFIS model with Gbell MF yielded a lower RMSE (0.0116) compared to Tri MF (0.0445), indicating better prediction accuracy and stability. The system consistently identified internal faults (output 1), and external faults (output 0) based on a 0.5 decision threshold. The findings validate the potential of ANFIS for integration into digital relay systems, enhancing real-time transformer protection through adaptive learning.

Downloads

Download data is not yet available.

References

[1] F. Gui, H. Chen, X. Zhao, P. Pan, C. Xin, and X. Jiang, “Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids,” Energies (Basel), vol. 17, no. 3, Jan. 2024, doi: 10.3390/en17030606.

[2] Md. A. H. S. Kamal and Md. S. Anower, “Design and Implementation of Differential Relay for Power Transformer Protection,” in 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh: IEEE, Jun. 2024, pp. 1–6. doi: 10.1109/ICAEEE62219.2024.10561871.

[3] D. A. Purba and U. A. Gani, “Study of Siemens 7UT86 Percentage Differential Relay as Protection of 60 MVA Power Transformer at 150 GIS kV Dayauh Kolot,” Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL), vol. 2, no. 2, pp. 208–223, Oct. 2024, doi: 10.26418/telectrical.v2i2.84598.

[4] M. C. Nițu, I. D. Nicolae, L. A. Dina, and P. M. Mircea, “Power Transformer Inrush Current Analysis: Simulation, Measurement and Effects,” Applied Sciences (Switzerland), vol. 14, no. 23, pp. 1–16, Dec. 2024, doi: 10.3390/app142310926.

[5] L. Wang, R. Liu, Y. Huang, Y. Li, and Y. Wang, “Simulation study on transformer inrush current and its suppression,” J Phys Conf Ser, vol. 2029, pp. 1–6, 2021, doi: 10.1088/1742-6596/2029/1/012010.

[6] M. A. M. Qasem and N. M. Nasr, “Effect of the Impulse Phenomenon on the Differential Protection of the Power Transformer,” SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE), vol. 5, no. 11, pp. 12–18, 2018, [Online]. Available: http://www.internationaljournalssrg.org

[7] “IEEE Guide for Protective Relay Applications to Power Transformers,” IEEE Std C37.91-2000 (Revision of IEEE Std C37.91-1985), pp. 1–85, 2000, doi: 10.1109/IEEESTD.2000.91943.

[8] I. Alhamrouni et al., “A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions,” Applied Sciences (Switzerland), vol. 14, no. 14, Jul. 2024, doi: 10.3390/app14146214.

[9] Y. Huang et al., “Artificial Intelligence for Resilient Power System: Motivations, Advances, and Challenges,” Smart Power & Energy Security, Jun. 2025, doi: 10.1016/j.spes.2025.06.001.

[10] H. Cao, C. Zhou, Y. Meng, J. Shen, and X. Xie, “Advancement in transformer fault diagnosis technology,” Front Energy Res, vol. 12, Jul. 2024, doi: 10.3389/fenrg.2024.1437614.

[11] A. N. Hamoodi, M. A. Ibrahim, and B. M. Salih, “An intelligent differential protection of power transformer based on artificial neural network,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 1, pp. 93–102, Feb. 2022, doi: 10.11591/eei.v11i1.3547.

[12] R. I. H. Hussein et al., “Enhanced Transformer Protection Using Fuzzy-Logic-Integrated Differential Relays: A Comparative Study with Rule-based Methods,” Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1299–1310, 2024, doi: 10.18196/jrc.v5i5.21937.

[13] L. D. Simões, H. J. D. Costa, M. N. O. Aires, R. P. Medeiros, F. B. Costa, and A. S. Bretas, “A power transformer differential protection based onsupport vector machine and wavelet transform,” Electric Power Systems Research, vol. 197, 2021, doi: 10.1016/j.epsr.2021.107297.

[14] P. Kumar Choudhary and D. Kumar Das, “Optimal Coordination of Over-current Relay Using Particle Swarm Optimization (PSO) Algorithm,” in Proceedings of 2020 IEEE Applied Signal Processing Conference, ASPCON 2020, Kolkata, India: Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 308–312. doi: 10.1109/ASPCON49795.2020.9276703.

[15] J.-S. R. Jang, “ANFIS : Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans Syst Man Cybern, vol. 23, no. 3, pp. 665–685, Jun. 1993, doi: 10.1109/21.256541.

[16] P. R. B. Dharmarajan, “Performance Analysis of Various Membership Functions using ANFIS for the Detection of Type-II Diabetes,” in 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia: IEEE, Sep. 2021. doi: 10.1109/GUCON50781.2021.9573682.

[17] Z. Janková and E. Rakovská, “Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market,” Applied Sciences (Switzerland), vol. 12, no. 2, Jan. 2022, doi: 10.3390/app12020918.

[18] S. Kanwal and S. Jiriwibhakorn, “Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods,” IEEE Access, vol. 12, pp. 49017–49033, 2024, doi: 10.1109/ACCESS.2024.3384761.

[19] S. Ahmad, A. A. Zakri, M. Oktaviandri, W. Sunanda, and A. Suryadi, “Penetration of DWT & ANFIS to Power Transmission Disturbances,” International Journal of Electrical, Energy and Power System Engineering, vol. 6, no. 1, pp. 105–112, Feb. 2023, doi: https://doi.org/10.31258/ijeepse.6.1.105-110.

[20] C. F. Mbey, V. J. Foba Kakeu, A. T. Boum, and F. G. Y. Souhe, “Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid,” The Journal of Engineering, vol. 2023, no. 11, 2023, doi: 10.1049/tje2.12324.

[21] A. Azhari Zakri, M. W. Mustafa, H. Firdaus, and I. Sofimieari, “Assess The Risk Level of Power Transformer Due Short-Circuit Faults Based on ANFIS,” SINERGI, vol. 23, no. 2, pp. 99–106, Jun. 2019, doi: 10.22441/sinergi.2019.2.002.

[22] M. Y. Suliman and M. T. Al-Khayyat, “Discrimination between inrush and internal fault currents in protection based power transformer using DWT,” International Journal on Electrical Engineering and Informatics, vol. 13, no. 1, pp. 1–20, Mar. 2021, doi: 10.15676/ijeei.2021.13.1.1.

[23] A. M. Salama, K. M. Abdel-Latif, M. M. Ismail, and S. M. Mousa, “A New Hybrid Protection Algorithm for Protection of Power Transformer Based on Discrete Wavelet Transform and ANFIS Inference Systems,” International Journal of Emerging Electric Power Systems, vol. 19, no. 3, 2018, doi: 10.1515/ijeeps-2017-0248.

[24] S. N. Sivanandam, S. N. Deepa, and S. Sumathi, Introduction to Fuzzy Logic Using MATLAB. Berlin: Springer, 2007.

[25] E. Andikan, “Detection And Classification of High Impedance Fault (HIF) In The 330kv High Voltage Power System Network in Nigerian with ANFIS Adaptive Neuro-Fuzzy Inference System (ANFIS),” Proceedings of International Conference on Scientific, Cultural Innovations and Sustainable Development, vol. 12, no. 3, pp. 33–42, 2020.

[26] M. M. Nezami, M. D. Equbal, S. A. Khan, and S. Sohail, “An ANFIS Based Comprehensive Correlation Between Diagnostic and Destructive Parameters of Transformer’s Paper Insulation,” Arab J Sci Eng, vol. 46, pp. 1541–1547, Jan. 2021, doi: 10.1007/s13369-020-05180-4.