Research Article | ![]()
Intelligent Thermal Protection of Power Transformers Using ANN–FLC Framework for Smart Grid Applications
Author(s): Salem Idham Ewad1*, Mohammed Brayyich2, and Adnan Shadhir Mutlaq3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 14, Issue 1
Publisher : FOREX Publication
Published : 30 March 2026
e-ISSN : 2347-470X
Page(s) : 200-206
Abstract
Transformer lifetime is greatly impacted by thermal stress under overload conditions, while traditional threshold-based protection and delayed cooling may react only after the hottest-spot temperature has already progressed to a critical stage. This study presents an adaptive framework to that combines ANN–FLC control with nanofluid cooling and Lyapunov-guided gain adaptation, while quantifying insulation loss of life based on the IEEE C57.91-2011 loading guide. The approach is evaluated on a 400 kVA, 33/0.38 kV, ONAN transformer under challenging conditions (40°C ambient, up to 140% overload, 5.2% THD, and a 15°C thermal step) each experiment was repeated five times (N = 1200 samples, 30 ms sampling). In comparison with ON–OFF, PID, and conventional ANN–FLC controllers. The proposed approach yields a faster transient response (28 s settling, 2.3°C overshoot), maintains operation within the 120°C limit, and decreasing aging-related indices. Long-period trajectories further present improved correlation aging and a projected insulation life of 32.4 years, enabling proactive operation/management, stability-aware transformer protection for intelligent-grid asset lifecycle management.
Keywords: Transformer, ANN, FLC, Protection, Smart Grid.
Salem Idham Ewad, Assistant Lecturer, Department of Electrical Engineering, University of Thi-Qar, Iraq; Email: salem.idham@utq.edu.iq
Mohammed Brayyich, Assistant Lecturer, Department of Electrical Engineering, University of Thi-Qar, Iraq; Email: m.r.brayyich@utq.edu.iq
Adnan Shadhir Mutlaq, Assistant Lecturer, Department of Electrical Engineering, University of Thi-Qar, Iraq; Email: adnan.shadhir@utq.edu.iq
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[1] Hao, Y., Zhang, Y., & Li, P. (2024). Inversion method for transformer winding hot-spot temperature based on SA-GRU model. Energy Reports, 10, 104328. https://doi.org/10.1016/j.egyr.2024.04.028
-
[2] Zhang, N., Liu, Q., Wang, M., & Zhao, F. (2024). Monitoring of transformer hotspot temperature using a wireless mesh network and SVR. Energies, 17(24), 6266. https://doi.org/10.3390/en17246266.
-
[3] Lessinger, S., Müller, P., & Hofmann, L. (2025). Aging assessment of power transformers with data science. Energies, 18(15), 3960. https://doi.org/10.3390/en18153960.
-
[4] lomgren, E. M. V. (2023). Grey-box modeling for hot-spot temperature prediction of power transformers. Electric Power Systems Research, 229, 109933. https://doi.org/10.1016/j.epsr.2023.109933
-
[5] Biswal, B., Deb, S., Datta, S., Ustun, T. S., & Cali, U. (2024). Review on smart grid load forecasting for energy management using machine learning and deep learning techniques. Energy Reports, 10, 653–672https://doi.org/10.1016/j.egyr.2024.09.056.
-
[6] Wazir, M. H., Mat Said, D., Md Sapari, N., & Mohd Yassin, Z. I. (2025). Electro-thermal modeling of distribution transformer for hottest-spot evaluation under PV-induced harmonics. International Journal of Renewable Energy Development, 14(3), 450–462. https://doi.org/10.61435/ijred.2025.60959.
-
[7] Li, D., Zhao, X., & Wang, Y. (2025). Digital analysis method of transformer hot-spot temperature based on two-way coupling flow–thermal model. IET Electrical Power Applications, 19(4), 1–12. https://doi.org/10.1049/elp2.70018.
-
[8] Makacha, J. M. (2025). An integrated fuzzy logic approach for valuation of power transformers (DOH/DOF). Engineering Reports, 7(3), 117030. https://doi.org/10.1002/eng2.117030.
-
[9] Singh, A. R., Kumar, D., & Patel, R. (2025). A deep learning and IoT-driven framework for real-time fault prediction in smart grids. Scientific Reports, 15, 2649. https://doi.org/10.1038/s41598-025-02649-w.
-
[10] Mohsen, S., Bajaj, M., Kotb, H., & Ghoneim, S. S. M. (2023). Efficient artificial neural network for smart grid stability prediction. International Transactions on Electrical Energy Systems, 33(4), e9974409. https://doi.org/10.1155/2023/9974409.
-
[11] Mharakurwa, E. T., Ndlovu, M., & Zhou, T. (2024). Transformer hot-spot temperature estimation through ANFIS model. Heliyon, 10(7), e23941. https://doi.org/10.1016/j.heliyon.2024.e23941.
-
[12] Yang, L., Chen, S., & Guo, F. (2025). A transformer oil temperature prediction method based on hybrid numerical and data-driven techniques. Processes, 13(2), 302. https://doi.org/10.3390/pr13020302.
-
[13] Prakash, A., Verma, S., & Das, P. (2024). Hybrid ANN–FLC-based control for improved transformer cooling. Journal of Electrical Engineering & Technology, 19(2), 234–245. https://doi.org/10.1007/s42835-024-01429-8.
-
[14] Kimura, H., Takahashi, T., & Sato, Y. (2024). Energy-efficient fuzzy logic cooling of distribution transformers. Energy Conversion and Economics, 5(1), 58–70. https://doi.org/10.3390/ece5010058.
-
[15] Kumar, N., Sharma, V., & Gupta, R. (2023). Thermal modeling and ANN optimization for transformer hot-spot control. Electric Power Components and Systems, 51(9), 1125–1142. https://doi.org/10.1080/15325008.2023.2287025.
-
[16] Sousa, R., Pereira, J., & Almeida, F. (2023). Fuzzy-based control for transformer thermal performance in hybrid renewable grids. Renewable Energy Focus, 45, 102–114. https://doi.org/10.1016/j.ref.2023.04.008.
-
[17] Khan, MA Masud, Roksana Haque, and Ammar Bajwa. "A Systematic Literature Review on Energy-Efficient Transformer Design For Smart Grids." American Journal of Scholarly Research and Innovation 1.01 (2022): 186-219.
-
[18] Taksana, Radomboon, et al. "Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis, Smooth Holtz–Winters, and L-BFGS for Smart Utility Control Centers." IEEE Access (2024).
-
[19] Ahmad, Azniza, et al. "Adaptive ANN based differential protective relay for reliable power transformer protection operation during energisation." IAES International Journal of Artificial Intelligence 8.4 (2019): 307
-
[20] da Costa Souza, João Pedro, et al. "A comprehensive review on artificial intelligence-based applications for transformer thermal modeling : Background and perspectives." IEEE Access (2024).

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