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Paper ID:IJSREM66111

Al-Based Smart Transformer Monitoring and Fault Detection System with 3D Wire Visualization and Automatic Power Cut - Off

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-

Subject:

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Paper ID:

IJSREM66111

Volume:

10 |

7 |

Jul 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Sharona A

Sharona A

Department of computer science engineering

Holycross engineering college

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Mahalakshmi K

Mahalakshmi K

Department of computer science engineering

Holycross engineering college

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DAMSS

Dr Arun Mozhi Selvi. S

Department of computer science engineering

Holycross engineering college

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Mahaboob Musbeera M

Mahaboob Musbeera M

Department of computer science engineering

Holycross engineering college

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Abstract

Distribution transformers typically exhibit warning signs prior to failure; however, many still experience explosions or outages only after significant damage has occurred. Common causes include overload current, prolonged overheating, and accidental wire damage. By the time manual inspection is conducted, faults often escalate into outages or, in severe cases, fire hazards. This paper introduces an Artificial Intelligence-based Smart Transformer Monitoring System that aims to detect such issues before they become critical. The system continuously monitors current, voltage, temperature, and wire-continuity data to identify abnormal behavior at an early stage. Upon confirmation of a fault or accident-related condition, such as a snapped wire, a relay automatically disconnects the power supply, and the Electricity Board (EB) cell receives an immediate alert with the precise fault location. For scalable monitoring, the wiring network is visualized on a 3D dashboard, where healthy lines are displayed in green and faulty lines in red, enabling EB staff to quickly identify issues across the grid without waiting for complaint calls.

KEYWORDS

Transformer Fault Detection Overload Protection Artificial Intelligence IoT Automatic Power Cut-off 3D Wire Visualisation Smart Grid.

Cite This Article

Sharona A, Mahalakshmi K, Dr Arun Mozhi Selvi. S, Mahaboob Musbeera M, "Al-Based Smart Transformer Monitoring and Fault Detection System with 3D Wire Visualization and Automatic Power Cut - Off," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 7, pp. 1-4, Jul 2026.

Full Article/Conclusion

This paper set out to close the gap between a transformer fault occurring and someone actually doing something about it, and the results suggest that combining continuous AI-based monitoring with automatic power isolation is a workable way to do that. Overload, overheating, and wire-cut conditions are caught early, power is cut before the situation turns dangerous, and the EB cell knows exactly where to send a team rather than searching for the problem. The 3D dashboard turned out to be more than a visual add-on — for a team monitoring dozens or hundreds of lines, a quick glance at red versus green is far more usable than scrolling through a log of alerts. Looking ahead, tying this into a city-wide SCADA system and using the accumulated sensor history for predictive maintenance scheduling seem like the natural next steps.

References

[1] S. V. Kulkarni and S. A. Khaparde, "Transformer Engineering: Design, Technology, and Diagnostics," CRC Press, 2012. [2]  IEEE Guide for Loading Mineral-Oil-Immersed Transformers, IEEE Std C57.91-2011. [3]   Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015. [4]   M. A. Taha, A. A. Youssef, and M. Mosaad, "IoT-Based Real-Time Transformer Health Monitoring System," IEEE Access, vol. 9, pp. 43522-43534, 2021. [5]    R. Vinayakumar et al., "Deep Learning Approach for Intelligent Fault Detection in Power Systems," IEEE Access, vol. 7, pp. 41525-41550, 2019. [6]   A. Shaban, M. F. Zia, and E. Elbouchikhi, "A Review of Machine Learning Approaches for Power Transformer Fault Diagnosis," Energies, vol. 14, no. 20, 2021. [7]    S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. [8]   M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, "Anomaly Detection: Methods, Systems and Tools," IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303-336, 2014. [9]  A. J. Bell and S. B. Wicker, "GPS-Enabled Fault Localization for Smart Distribution Grids," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2765-2774, 2019. [10]  T. Sousa, T. Soares, and Z. Vale, "Smart Grid Monitoring and Visualization Platforms: A Review," Renewable and Sustainable Energy Reviews, vol. 91, pp. 1136-1147, 2018.

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Publication Date

7/16/2026

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Review Rounds: 2

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