Early Detection of Short Circuit Faults Between Windings in Distribution Transformers Using Finite Element Method

Authors

DOI:

https://doi.org/10.18196/jrc.v6i4.26004

Keywords:

Distribution Transformers, Internal Short Circuit Fault Simulation, Finite Element Method (FEM), ANSYS Maxwell Software, Magnetic Flux Distribution Analysis, Predictive Fault Diagnosis

Abstract

The primary aim and contribution of this study is the presentation of a non-intrusive early diagnosis method based on finite element simulation (FEM). The focus was on a 1000 kVA distribution transformer based on manufacturing data and field tests conducted in Mosul, Iraq. An accurate two-dimensional model of the transformer was developed using ANSYS Maxwell software, simulating normal operation and various internal fault scenarios (such as single-phase or double-phase short-circuits and ground faults) at varying rates. The resulting changes in magnetic flux distribution, core losses, currents, and voltages were analyzed as indicators to determine the presence, type, and severity of faults. A representation of internal faults in the three-phase transformer windings was performed to detect and diagnose faults early. The results clearly show that small short-circuit faults (up to 1.2% of the windings) are distinguishable by specific changes in transformer parameters. These faults lead to a localized temperature increase and the onset of insulation deterioration. It was also observed that an increase in the fault percentage (5% to 25%) causes a significant increase in magnetic flux and total losses. These effects are significantly exacerbated by ground faults or faults involving two phases. These results confirm that computational analysis provides a powerful tool for proactive monitoring, enabling preventive maintenance scheduling based on initial fault indications. This contributes to extending transformer life, enhancing network reliability, and avoiding costly catastrophic failures. Continuous monitoring and effective ground protection remain critical elements for maintaining transformer safety and efficiency.

References

T. Meng et al., “Research on distribution of winding leakage magnetic field of three-phase dry type transformer under short-circuit condition,” Energy Reports, vol. 9, pp. 1108–1115, 2023.

J. P. Américo, J. V. Leite, and C. F. Mazzola, “Enhanced thermal modeling of three-phase dry-type transformers,” Case Studies in Thermal Engineering, vol. 58, 2024.

B. Xie, D. Zhao, and T. Hong, “Transformer monitoring and protection in dynamic power systems – A review,” Frontiers in Energy Research, vol. 8, pp. 1–15, 2020.

M. H. Mahmood and S. A. Makki, “Power distribution transformers monitoring based on Zigbee and sensors technology,” in Proc. 7th Int. Eng. Conf. ‘Research and Innovation Amid Global Pandemic’ (IEC), 2021, pp. 112–117, doi: 10.1109/IEC52205.2021.9476136.

Y. Biçen and F. Aras, “Smart asset management system for power transformers coupled with online and offline monitoring technologies,” Engineering Failure Analysis, vol. 154, 2023.

B. Raja, G. R. Venkatakrishnan, and R. Rengaraj, “Power transformer fault diagnosis and condition monitoring using hybrid TDO-SNN technique,” International Journal of Hydrogen Energy, vol. 68, pp. 1370–1381, 2024.

F. Samanlioglu and Z. Ayag, “An intelligent approach for the evaluation of transformers in a power distribution project,” Journal of Intelligent and Fuzzy Systems, vol. 39, pp. 4133–4145, 2020.

J. Z. Balanta, S. Rivera, A. A. Romero, and G. Coria, “Planning and optimizing the replacement strategies of power transformers: Literature review,” Energies, vol. 16, 2023.

Z. Li, W. Chen, X. Yan, Q. Zhou, and H. Wang, “An outlier robust detection method for online monitoring data of dissolved gases in transformer oils,” Flow Measurement and Instrumentation, vol. 102, 2025.

R. Soni and B. Mehta, “A review on transformer condition monitoring with critical investigation of mineral oil and alternate dielectric fluids,” Electric Power Systems Research, vol. 214, 2023.

M. S. Katooli and A. Koochaki, “Detection and classification of incipient faults in three-phase power transformer using DGA information and rule-based machine learning method,” Journal of Control, Automation and Electrical Systems, vol. 31, pp. 1251–1266, 2020.

Z. Kazemi, F. Naseri, M. Yazdi, and E. Farjah, “An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers,” IET Science, Measurement and Technology, vol. 15, pp. 130–142, 2021.

S. R. Pani, P. K. Bera, and V. Kumar, “Detection and classification of internal faults in power transformers using tree based classifiers,” in Proc. 9th IEEE Int. Conf. Power Electronics, Drives and Energy Systems (PEDES), 2020, doi: 10.1109/PEDES49360.2020.9379641.

R. Soni and B. Mehta, “Diagnosis and prognosis of incipient faults and insulation status for asset management of power transformer using fuzzy logic controller & fuzzy clustering means,” Electric Power Systems Research, vol. 220, 2023.

Q. Wang, S. Wang, and Y. Yang, “Fault diagnosis of power transformer based on RVFL model,” in Proc. 3rd IEEE Conf. Energy Internet and Energy System Integration (EI2), pp. 788–793, 2019, doi: 10.1109/EI247390.2019.9061832.

M. Tabrez et al., “Equivalent circuit modelling of a three-phase to seven-phase transformer using PSO and GA,” Journal of Intelligent and Fuzzy Systems, vol. 42, pp. 689–698, 2022.

V. S. B. C. Duvvury and S. Pramanik, “An attempt to identify the faulty phase in three-phase transformer windings using an advanced FRA measurement technique,” IEEE Transactions on Power Delivery, vol. 36, pp. 3162–3171, 2021.

P. K. Bera, C. Isik, and V. Kumar, “Discrimination of internal faults and other transients in an interconnected system with power transformers and phase angle regulators,” IEEE Systems Journal, vol. 15, pp. 3450–3461, 2021.

J. Liu et al., “Moisture diagnosis of transformer oil-immersed insulation with intelligent technique and frequency-domain spectroscopy,” IEEE Transactions on Industrial Informatics, vol. 17, pp. 4624–4634, 2021.

S. Brodeur and J. B. Dastous, “Design and testing of an arc resistant power transformer tank,” IEEE Transactions on Power Delivery, vol. 35, pp. 699–706, 2020.

Z. Zhao et al., “Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature,” IEEE Access, vol. 7, pp. 40463–40474, 2019.

K. R. Hameed, A. L. Suraiji, and A. O. Hanfesh, “Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM,” Open Engineering, vol. 13, 2023.

C. Yan, P. Zhang, Y. Xu, and J. Shu, “Modeling and simulation of transformer inter-turn short-circuit faults based on field-circuit coupling,” in Proc. 20th Biennial IEEE Conf. Electromagn. Field Comput. (CEFC), 2022, doi: 10.1109/CEFC55061.2022.9940726.

E. S. M. El-kenawy et al., “Feature selection and classification of transformer faults based on novel meta-heuristic algorithm,” Mathematics, vol. 10, 2022.

Y. Wu, X. Sun, Y. Zhang, X. Zhong, and L. Cheng, “A power transformer fault diagnosis method-based hybrid improved seagull optimization algorithm and support vector machine,” IEEE Access, vol. 10, pp. 17268–17286, 2022.

Y. Özüp̧ak, “Performing structural design and modeling of transformers using ANSYS-Maxwell,” Brilliant Engineering, vol. 2, pp. 38–42, 2021.

K. Huang et al., “Three methods to simulate transformer zero-sequence impedance with Ansys Maxwell,” in Proc. 5th Int. Conf. Power Energy Technol. (ICPET), pp. 153–158, 2023, doi: 10.1109/ICPET59380.2023.10367486.

A. Giri, R. Darnal, R. Kumar, and A. K. Singh, “Design and simulation of medium-frequency transformer in Ansys Maxwell software,” Lecture Notes in Electrical Engineering, vol. 1037, pp. 491–500, 2023.

P. Ggutte, A. R. Phadke, U. Sanvatsarkar, and S. Haribaskhar, “Design of 145 kV OIP based transformer bushing using ANSYS Maxwell,” in Proc. Int. Conf. Emerg. Trends Eng. Med. Sci. (ICETEMS), pp. 258–263, 2022, doi: 10.1109/ICETEMS56252.2022.10093466.

M. Gadala, Finite Elements for Engineers with ANSYS Applications. Cambridge, U.K.: Cambridge Univ. Press, 2020, doi: 10.1017/9781108151689.

W. Xing, H. Xu, and L. Kuo, "Mathematical Model and Simulation Based on ANSYS Maxwell of the Magnetic Fluid Differential Transformer Inclination Sensor," Journal of Engineering Research and Reports, pp. 1–9, 2023, doi: 10.9734/jerr/2023/v24i3803.

P. Bai and Y. Jia, "Simulation analysis of internal and external faults in transformer area," MATEC Web of Conferences, vol. 382, p. 01041, 2023.

O. S. Al-Yozbaky and R. A. Othman, "The Influence of Non-Sinusoidal Power Supply on Single-Phase Transformer Performance," Journal Européen des Systèmes Automatisés, vol. 57, p. 1015, 2024.

S. S. Mopari, D. S. More, P. K. Murthy, and M. P. S. Chawla, "Estimation of Inductance and Capacitance Parameters of Single Phase Dual Winding Converter Transformer with FEM," in Proc. 2022 IEEE 11th Int. Conf. Commun. Syst. Netw. Technol. (CSNT), pp. 83–88, 2022, doi: 10.1109/CSNT54456.2022.9787659.

S. Kul, M. F. Aslan, and S. S. Tezcan, "Core Loss Estimation for Three Phase Transformer Based on GPR and FEA," Advances in Transdisciplinary Engineering, vol. 58, pp. 536–546, 2024.

S. Balci, "Thermal behavior of a three phase isolation transformer under load conditions with the finite element analysis," Thermal Science, vol. 24, pp. 2189–2201, 2020.

S. Kul, S. Balci, and S. S. Tezcan, "Output voltage estimation of a power transformer integrated with three-phase T-type inverter," Journal of Energy Systems, vol. 7, pp. 199–211, 2023.

M. A. Kolobov, A. V. Okunev, and D. V. Bushmanov, "Research of planar transformer properties using ansys software," in Proc. 2020 Int. Conf. Ind. Eng., Appl. Manuf. (ICIEAM), 2020, doi: 10.1109/ICIEAM48468.2020.9111980.

Y. Cetinceviz and E. Sehirli, "Design and numerical analysis of thermal capability of power transformer using coupled electromagnetic field-thermal model," Electrical Engineering, vol. 106, pp. 4821–4834, 2024.

A. El Shafei, S. Ozdemir, N. Altin, G. Jean-Pierre, and A. Nasiri, "Design and Implementation of a Medium Voltage, High Power, High Frequency Four-Port Transformer," in Proc. IEEE Appl. Power Electron. Conf. Expo. (APEC), pp. 2352–2357, 2020.

D. K. Kohar, A. Biswas, A. K. Pati, and A. Kumar, "Design Approaches of Wireless Power Transfer (WPT) Coil using ANSYS Maxwell SIMPLORER," in Proc. 2022 IEEE Int. Conf. Current Develop. Eng. Technol. (CCET), 2022, doi: 10.1109/CCET56606.2022.10080516.

N. Lin, P. Liu, and V. Dinavahi, "Component-Level Thermo-Electromagnetic Nonlinear Transient Finite Element Modeling of Solid-State Transformer for DC Grid Studies," IEEE Trans. Ind. Electron., vol. 68, pp. 938–948, 2021.

F. Alpsalaz and M. S. Mamiş, "Detection of Arc Faults in Transformer Windings via Transient Signal Analysis," Applied Sciences (Switzerland), vol. 14, 2024.

S. Saha, R. Sahoo, and M. Roy, "Designing of Transformer for DC Flyback Converter," in Proc. 2023 11th Nat. Power Electron. Conf. (NPEC), 2023, doi: 10.1109/NPEC57805.2023.10384977.

S. M. Diga et al., "Some Considerations on the Dimensioning of Ancillary Service Transformers of Power Plants," in Proc. 2024 Int. Conf. Appl. Theor. Electricity (ICATE), 2024, doi: 10.1109/ICATE62934.2024.10749111.

O. S. Alyozbaky, "The losses and temperature comparison in three-phase distribution transformer with various assembly core designs," Australian Journal of Electrical and Electronics Engineering, vol. 15, pp. 61–70, 2018.

Z. Dobesova, "Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics," Computer Applications in Engineering Education, vol. 32, 2024.

R. Ratra and P. Gulia, "Experimental evaluation of open source data mining tools (WEKA and orange)," International Journal of Engineering Trends and Technology, vol. 68, pp. 30–35, 2020.

A. Ishak, K. Siregar, Asfriyati, R. Ginting, and M. Afif, "Orange Software Usage in Data Mining Classification Method on the Dataset Lenses," IOP Conference Series: Materials Science and Engineering, vol. 1003, 2020.

J. Ramesh, S. Shahriar, A. R. Al-Ali, A. Osman, and M. F. Shaaban, "Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System," Energies, vol. 15, 2022.

M. Yildirim et al., “Nonlinear optical data transformer for machine learning,” arXiv preprint arXiv:2208.09398, 2022.

M. Shulajkovska, M. Smerkol, G. Noveski, and M. Gams, “Enhancing urban sustainability: Developing an open-source AI framework for smart cities,” Smart Cities, vol. 7, pp. 2670–2701, 2024.

N. Pillai et al., “EndToEndML: An open-source end-to-end pipeline for machine learning applications,” in Proc. 7th Int. Conf. Inf. Comput. Technol. (ICICT), pp. 350–358, 2024, doi: 10.1109/ICICT62343.2024.00063.

A. S. Altaie, M. Abderrahim, and A. A. Alkhazraji, “Transmission line fault classification based on the combination of scaled wavelet scalograms and CNNs using a one-side sensor for data collection,” Sensors, vol. 24, 2024.

M. Safdar et al., “Accelerated semantic segmentation of additively manufactured metal matrix composites: Generating datasets, evaluating convolutional and transformer models, and developing the MicroSegQ+ tool,” Expert Syst. Appl., vol. 251, 2024.

Z. Li, Z. Jiao, and A. He, “Knowledge-based artificial neural network for power transformer protection,” IET Gener., Transmiss. Distrib., vol. 14, pp. 5816–5822, 2020.

M. Tahir and S. Tenbohlen, “Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements,” Energies, vol. 14, 2021.

Y. Zhang, Y. Tang, Y. Liu, and Z. Liang, “Fault diagnosis of transformer using artificial intelligence: A review,” Front. Energy Res., vol. 10, 2022.

D. Santamargarita, D. Molinero, E. Bueno, M. Marron, and M. Vasic, “On-line monitoring of maximum temperature and loss distribution of a medium frequency transformer using artificial neural networks,” IEEE Trans. Power Electron., vol. 38, pp. 15818–15828, 2023.

B. P. Bhattarai et al., “Big data analytics in smart grids: State-of-the-art, challenges, opportunities, and future directions,” IET Smart Grid, vol. 2, pp. 141–154, 2019.

Z. Lin, S. Tang, G. Peng, Y. Zhang, and Z. Zhong, “An artificial neural network model with Yager composition theory for transformer state assessment,” in Proc. IEEE 2nd Adv. Inf. Technol., Electron. Autom. Control Conf. (IAEAC), pp. 652–655, 2017, doi: 10.1109/IAEAC.2017.8054097.

A. Abu-Siada, “Improved consistent interpretation approach of fault type within power transformers using dissolved gas analysis and gene expression programming,” Energies, vol. 12, 2019.

S. Patel, A. Derwal, S. Doshi, and S. K. Rajendra, “Utilising genetic algorithm and driving point impedance data to synthesize high frequency circuit model of power transformer winding,” in Proc. 5th Int. Conf. Signal Process. Integr. Netw. (SPIN), pp. 407–412, 2018, doi: 10.1109/SPIN.2018.8474058.

Y. Wu, X. Sun, P. Yang, and Z. Wang, “Transformer fault diagnosis based on improved particle swarm optimization to support vector machine,” J. Phys.: Conf. Ser., vol. 1750, 2021.

A. M. Aciu, M. C. Nițu, C. I. Nicola, and M. Nicola, “Determining the remaining functional life of power transformers using multiple methods of diagnosing the operating condition based on SVM classification algorithms,” Machines, vol. 12, 2024.

H. Du et al., “A method for identifying external short-circuit faults in power transformers based on support vector machines,” Electronics (Switzerland), vol. 13, 2024.

T. Shanu and A. Mishra, “Wavelet scattering and multiclass support vector machine (WS_MSVM) for effective fault classification in transformers: A real-time experimental approach,” Eng. Res. Express, vol. 6, 2024.

S. R. Mestha and N. Prabhu, “Support vector machine based fault detection in inverter-fed electric vehicle,” Energy Storage, vol. 6, 2024.

A. Nanfak et al., “A combined technique for power transformer fault diagnosis based on k-means clustering and support vector machine,” IET Nanodielectrics, 2024, doi: 10.1049/nde2.12088.

D. A. Barkas, I. Chronis, and C. Psomopoulos, “Failure mapping and critical measurements for the operating condition assessment of power transformers,” Energy Rep., vol. 8, pp. 527–547, 2022.

J. Singh, S. Singh, and A. Singh, “Distribution transformer failure modes, effects and criticality analysis (FMECA),” Engineering Failure Analysis, vol. 99, pp. 180–191, 2019.

S. Brodeur, V. N. Lê, and H. Champliaud, “A nonlinear finite-element analysis tool to prevent rupture of power transformer tank,” Sustainability (Switzerland), vol. 13, 2021.

S. M. A. Alkahdely and A. N. B. Alsammak, “Non-limiting operation of the on-load tap changing transformer, and its effect on voltage stability, with regards to the Nineveh electrical grid,” Przeglad Elektrotechniczny, vol. 99, pp. 181–186, 2023.

A. Abdali et al., “Magnetic-thermal analysis of distribution transformer: Validation via optical fiber sensors and thermography,” International Journal of Electrical Power and Energy Systems, vol. 153, p. 109346, 2023.

A. F. Hacan, B. Kabas, and S. Oguten, “Design optimization of a three-phase transformer using finite element analysis,” arXiv preprint arXiv:2201.11769, 2022.

Y. Özüpak, “Performing structural design and modeling of transformers using ANSYS-Maxwell,” Brilliant Engineering, vol. 2, pp. 38–42, 2021.

S. Bal, T. Demirdelen, and M. Tumay, “Three-phase distribution transformer modeling and electromagnetic transient analysis using ANSYS Maxwell,” in Proc. 3rd Int. Symp. Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 6–9, 2019, doi: 10.1109/ISMSIT.2019.8932953.

K. Dawood, G. Komurgoz, and F. Isik, “Modeling of distribution transformer for analysis of core losses of different core materials using FEM,” in Proc. 8th Int. Conf. Modeling Simulation and Applied Optimization (ICMSAO), pp. 1–5, 2019, doi: 10.1109/ICMSAO.2019.8880392.

S. Kaur and D. Kaur, “3-D comparative analysis of 3-phase transformer core using CRGO silicon steel, amorphous and FINEMET for low losses using ANSYS,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 872, 2020.

B. Hashemi, A. A. Taheri, and F. Jozi, “Analysis of transformer no-load loss using finite element method validated by experimental tests,” Available at SSRN 4760505, pp. 1–14, 2024.

Downloads

Published

2025-08-10

How to Cite

[1]
M. T. Aljammal and O. S. Alyozbaky, “Early Detection of Short Circuit Faults Between Windings in Distribution Transformers Using Finite Element Method”, J Robot Control (JRC), vol. 6, no. 4, pp. 2095–2108, Aug. 2025.

Issue

Section

Articles