IOT Integrated Transformer Health Monitoring and Predictive Maintenance System

Abstract: Power distribution networks depend heavily on transformers, and their failure can seriously interrupt operations. In order to track transformer health and forecast maintenance requirements, this study presents a smart system that integrates IoT technologies. It makes defect detection and condition assessment possible in real time. The system uses a variety of sensors to track important metrics like voltage, current, power, energy, frequency, power factor, and temperature. Data is sent wirelessly through an ESP8266 to the ThingSpeak cloud, where it’s analyzed for any irregularities. A machine learning model, trained on historical sensor data, helps predict potential faults by using set thresholds, which aids in early detection and preventive maintenance. To make it even better, the system features a user-friendly dashboard built with Streamlit that offers real-time monitoring, fault classification, and instant alerts. It also explores different communication technologies like GSM, LoRa, Zigbee, and Bluetooth to ensure reliable data transmission. The Random Forest model achieves an impressive 99.2% classification accuracy, demonstrating the system's remarkable fault prediction accuracy. This method reduces maintenance costs, increases transformer dependability, and helps avoid power outages by combining IoT, cloud computing, and AI analytics. In the future, three-phase industrial transformers may benefit from improved scalability and faster fault detection with the integration of edge computing