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Paper ID:IJSREM67639

Smart Transformer with IoT-Based Power Monitoring and Automatic Load Balancing System for Industrial Applications

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67639

Volume:

10 |

10 |

Oct 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67639

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

PN

Pratiksha Nagpure

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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SD

Sanket Dhok

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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SD

Shweta Deshmukh

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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RB

Riddhi Bhure

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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SN

Shrutika Neware

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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Mentors

PLMB

Prof. L. M. Bopche

Department of Electrical Engineering

Priyadarshini College of Engineering, Nagpur, Maharashtra, India.

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Abstract

The increasing and continuously varying power demand in industrial applications can cause transformer overloading, excessive temperature rise, equipment damage, and unexpected power interruptions. This paper presents an IoT-based Smart Transformer with Power Monitoring and Automatic Load Balancing System using two transformers for reliable industrial power distribution. Under normal load conditions, Transformer 1 supplies the required power while Transformer 2 remains in standby mode. When the load exceeds a predefined threshold, the Arduino controller automatically activates Transformer 2 to share the additional load, thereby reducing the loading stress on the first transformer. Voltage and current sensors continuously measure electrical parameters, while a temperature sensor monitors transformer thermal conditions. The measured parameters are processed by an Arduino UNO and displayed locally on an LCD. An ESP8266 IoT module transmits real-time voltage, current, temperature, power, and transformer operating status to a cloud platform for remote monitoring. An automatic cooling fan is activated when the transformer temperature exceeds the specified safety limit. The proposed system combines automatic load management, thermal protection, local monitoring, and IoT-based remote supervision. The approach can improve transformer utilization, reduce overheating risks, support preventive maintenance, and enhance reliability and safety in industrial power distribution systems.

KEYWORDS

Smart Transformer IoT Monitoring Load Balancing Temperature Protection Power Monitoring etc

Cite This Article

Pratiksha Nagpure, Sanket Dhok, Shweta Deshmukh, Riddhi Bhure, Shrutika Neware, Prof. L. M. Bopche, "Smart Transformer with IoT-Based Power Monitoring and Automatic Load Balancing System for Industrial Applications," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-6, Oct 2026. https://doi.org/10.55041/IJSREM67639

Full Article/Conclusion

The proposed Smart Transformer with IoT-Based Power Monitoring and Automatic Load Balancing System provides an integrated approach for improving transformer operation in industrial applications. The developed concept uses two 0.5 kVA, 230/12 V transformers, Arduino UNO, voltage and current sensors, a DS18B20 temperature sensor, relay modules, LCD display, cooling fan, and ESP8266 IoT connectivity. Under normal loading, Transformer 1 supplies the load while Transformer 2 remains OFF. When the load exceeds the predefined 400 VA threshold, Transformer 2 is automatically activated to share the load. For the considered 700 VA high-load condition, both transformers share approximately 350 VA each, corresponding to about 70% loading and 29.17 A secondary current per transformer. The system also provides continuous monitoring of voltage, current, power, and temperature. Automatic cooling is incorporated to respond to high-temperature conditions, while the LCD enables local observation and the IoT module supports remote monitoring through the cloud. Therefore, the proposed system demonstrates a practical framework for automatic load management, transformer protection, and real-time supervision. The calculated results support the feasibility of the proposed prototype, while actual hardware testing will be required to validate sensor accuracy, switching performance, thermal response, and IoT communication under practical operating conditions.

References

[1]  M. R. Msane, B. A. Thango, and K. A. Ogudo, “Condition monitoring of electrical transformers using the Internet of Things: A systematic literature review,” Applied Sciences, vol. 14, no. 21, Art. no. 9690, 2024, doi: 10.3390/app14219690. [2]  L. Jin, D. Kim, A. Abu-Siada, and S. Kumar, “Oil-immersed power transformer condition monitoring methodologies: A review,” Energies, vol. 15, no. 9, Art. no. 3379, 2022, doi: 10.3390/en15093379. [3] V. A. Thiviyanathan, P. J. Ker, Y. S. Leong, F. Abdullah, A. Ismail, and M. Z. Jamaludin, “Power transformer insulation system: A review on the reactions, fault detection, challenges and future prospects,” Alexandria Engineering Journal, vol. 61, no. 10, pp. 7697–7713, 2022, doi: 10.1016/j.aej.2022.01.026. [4]  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, Art. no. 108954, 2023, doi: 10.1016/j.epsr.2022.108954. [5]  L. Jin, D. Kim, and A. Abu-Siada, “State-of-the-art review on asset management methodologies for oil-immersed power transformers,” Electric Power Systems Research, vol. 218, Art. no. 109194, 2023, doi: 10.1016/j.epsr.2023.109194. [6]   A. Dhiman and R. Perveen, “UPIoT based power monitoring of transformer,” Materials Today: Proceedings, vol. 71, no. 2, pp. 227–231, 2022, doi: 10.1016/j.matpr.2022.08.511. [7]  M. Elsisi, M.-Q. Tran, K. Mahmoud, D.-E. A. Mansour, M. Lehtonen, and M. M. F. Darwish, “Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties,” Measurement, vol. 190, Art. no. 110686, 2022, doi: 10.1016/j.measurement.2021.110686. [8]   R. R. Singh, G. Bhatti, and D. Saravanan, “New-age condition monitoring of on-load tap changing transformers in distributed energy systems for Industry 4.0,” e-Prime—Advances in Electrical Engineering, Electronics and Energy, vol. 2, Art. no. 100087, 2022, doi: 10.1016/j.prime.2022.100087. [9] M. Beheshti Asl, I. Fofana, and F. Meghnefi, “Review of various sensor technologies in monitoring the condition of power transformers,” Energies, vol. 17, no. 14, Art. no. 3533, 2024. [10]  V. M. N. Dladla and B. A. Thango, “Fault classification in power transformers via dissolved gas analysis and machine learning algorithms: A systematic literature review,” Applied Sciences, vol. 15, no. 5, Art. no. 2395, 2025.

Editorial & Review Transparency

Publication Date

10/5/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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