An Approach for Real-Time Distribution Transformer Health Monitoring and Predictive Maintenance System

Power distribution transformers are critical assets in different electrical infrastructure. Sometimes the unexpected failures lead to prolonged outages, economic losses, and safety risks than can't be resolved on use of traditional monitoring due to mechanical relays and manual inspections. Also, it fails in real-time fault prediction. This paper presents a modern, low-cost IoT-based system for real-time monitoring of key transformer parameters including voltage, current, oil temperature, winding temperature, oil level, humidity, vibration, and dissolved gases. The system uses an ESP32 micro-controller for real time data acquisition and prepossessing. For long-range, low-power wireless communication with adaptive data rates Zigbee platform is utilized. It can satisfy the IoT based communication, visualization and predictive analysis. With protective actions. Similarly, the data communication can be performed using SMS, email, and push notification. Integration of a hybrid machine learning model as Dynamic Multi scale Attention CNN-LSTM, DMSA-CNN-LSTM) enables predictive maintenance with multi-modal data fusion, achieving 99.17% classification accuracy, 97.14% F1-score, 97.81% precision, and 96.47% recall on fused datasets. The proposed design outperforms in terms of range, power efficiency, scalability, cost, and aligning with recent advancements in smart grid applications, including AI-driven anomaly detection and digital twins.