Electrical power transformers are critical and expensive assets in power distribution networks, vulnerable to failures from electrical, thermal, and mechanical stresses. Unplanned outages result in significant financial losses and safety hazards. This research presents the design and implementation of a comprehensive, low-cost transformer health monitoring system utilizing an ESP32 microcontroller and Internet of Things (IoT) technology. The system continuously monitors five key parameters—voltage, current, temperature, humidity, and vibration—using dedicated sensors: ZMPT101B, ACS712, DHT22, and SW-420. Data is processed by the ESP32 and transmitted in real-time to the Blynk cloud platform via Wi-Fi. The system implements a robust fault detection algorithm with configurable thresholds and software debounce logic. Upon detecting an anomaly (e.g., overvoltage, overload, overheating), it triggers a local buzzer, displays the fault on a 16×2 LCD, and sends an instant push notification to a remote operator. Hardware validation confirmed accurate sensor readings and a 100% success rate in detecting six simulated fault conditions within 3–10 seconds. With a total hardware cost, this system offers a scalable, cost-effective solution for proactive transformer maintenance, enabling a shift from reactive, periodic inspections to continuous, real-time monitoring.

This paper presented the successful design, implementation, and validation of a low-cost, ESP32-based IoT system for comprehensive transformer health monitoring. The system simultaneously tracks five critical parameters—voltage, current, temperature, humidity, and vibration—enabling a holistic assessment of transformer condition. The implemented fault detection algorithm with software debouncing ensures reliable alerting without false positives. Integration with the Blynk cloud platform provides real-time remote visualization and instant push notification to field engineers, irrespective of their location. Laboratory testing confirmed the system’s functionality, with accurate live readings and 100% detection of all simulated fault conditions within 3–10 seconds. With a total hardware cost of approximately ₹1,500 per unit, the proposed system offers a transformative, economically viable solution for enabling predictive maintenance of distribution transformers. It effectively bridges the gap between the need for continuous monitoring and the cost constraints of large-scale deployment, particularly in rural and semi-urban power networks.

1.Condition Monitoring of Electrical Transformers Using the Internet of Things: A Systematic Literature Review,” MDPI Applied Sciences, vol. 14, no. 21, p. 9690, 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/21/9690 2.”IoT Based Transformer Monitoring System using ESP-32,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 11, no. 3, pp. 1234–1240, Mar. 2023. [Online]. Available: https://www.ijraset.com/best-journal/iot-based-transformer-monitoring-system-using-esp-32 3.”A Review Paper on Transformer Health Monitoring System,” Advancement of IoT in Blockchain Technology and its Applications (AIBTIA), Mat Journals, e-ISSN: 2583-7826, 2023. 4.A. K. Verma and S. Singh, “Real-Time Transformer Monitoring System Using IoT Sensors,” International Journal of Engineering Research and Technology (IJERT), vol. 9, no. 2, pp. 45–51, 2020. 5.”Smart Transformers Health Monitoring Using AI-ML And IoT Integration,” International Journal of Science, Engineering and Technology (IJSET), vol. 5, no. 3, pp. 101–108, 2023. 6.Espressif Systems. ESP32 Series Datasheet. [Online]. Available: https://www.espressif.com/sites/default/files/documentation/esp32_datasheet_en.pdf 7.K. Hussain, A. Sheikh, and S. Laxman, “Real-Time IoT-Based Automation System Using Wi-Fi Microcontrollers for Industrial Monitoring,” International Journal of Internet of Things and Applications, vol. 6, no. 2, pp. 98–106, 2020. 8.Blynk. Blynk Documentation. [Online]. Available: https://docs.blynk.io/ 9.MathWorks. ThingSpeak™ IoT Analytics. [Online]. Available: https://thingspeak.com/ 10.M. Gupta, S. Saxena, and R. Jain, “Performance Analysis of Sensors for Electrical Parameter Monitoring in Microcontroller-Based Systems,” Journal of Sensor Technology and Applications, vol. 7, no. 1, pp. 55–63, 2018. M. Desai and K. P. Shah, “IoT-Based Fault Detection System for Electrical Transformers in Distribution Networks,” International Journal of Green Computing, vol. 5, no. 3, pp. 95–103, 2019.

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