Distribution transformers are among the most vital and capital-intensive assets of a power network, yet they are still maintained through periodic manual inspections that cannot detect rapid, incipient faults. This paper presents an Internet of Things (IoT) based system for continuous transformer health monitoring and fault diagnosis. A NodeMCU ESP8266 acts as the main controller and collects data from a DHT11 temperature and humidity sensor, an SW-420 vibration sensor, and an LM393-based acoustic sound sensor mounted on a 12-0-12 V, 2 A step-down transformer. The controller compares the readings with predefined thresholds and transmits the processed data over Wi-Fi to the Blynk IoT Cloud, where gauges, charts, and status widgets display the live condition of the transformer. Push notifications and e-mail alerts are generated automatically when a threshold is crossed, so that engineers are informed before a catastrophic failure occurs. During testing under progressive loading, the dashboard reflected a temperature rise within 1.2 seconds and the alerts were delivered with no packet loss. The system offers a low-cost alternative to expensive SCADA-based solutions for predictive maintenance.

Keywords:    Transformer    Monitoring,    IoT, NodeMCU     ESP8266,     Blynk,     Predictive

Maintenance.