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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM60053
Volume:
10 |
4 |
April 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
The power distribution transformers are one of the most crucial pieces of equipment used in electricity grids across the world. Failure of such devices due to any reasons ranging from electrical issues to thermal problems or even to theft may cause outages for many consumers, and result in losses. Transformer theft is still rampant in India and other developing countries, thus highlighting the need for remote monitoring systems. This paper describes the development of a robust and cost-effective IoT-based solution for the purpose of real-time monitoring and protection of power distribution transformers. The proposed system employs an Arduino Uno as the controlling device, along with various types of sensors, including ACS712 Current Sensor, Voltage Divider, DS18B20 Temperature sensor, HC-SR04 Ultrasonic Sensor for estimation of oil level, and ADXL345 Accelerometer for detecting physical tampering of the transformer. A SIM800A module is used to send SMSs to maintenance personnel about potential threats to the transformerâs operation, whereas the NodeMCU (ESP8266) module sends sensor readings online to the ThingSpeak IoT cloud platform. Six parameters are continuously monitored by the system, namely: supply voltage, load current, transformer oil temperature, oil level, vibration/acceleration, and thermal status. In case of occurrence of any of these faults, threshold-based fault detection enables the system to automatically shut off the transformer via relay switching along with an alarm signal through the buzzer. The experimental findings revealed that the system was able to detect the occurrence of all faults including high voltage, low voltage, overload, high temperature, low oil level, and theft. Visualization of data was done on ThingSpeak dashboard throughout the experimentation period (March 25-April 04, 2026).
KEYWORDS
Publication Date
4/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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