IoT-Based Predictive Health Monitoring Framework for Smart Transformer Diagnostics in Power Systems

Abstract

The reliability and continuous operation of power transformers are essential for maintaining stability and efficiency in modern power systems. Conventional transformer maintenance approaches primarily rely on periodic inspections and reactive fault detection methods, which often lead to unexpected failures, increased maintenance costs, and reduced operational reliability. This research proposes an IoT-based predictive health monitoring framework for smart transformer diagnostics in power systems to enable real-time condition monitoring and early fault prediction. The proposed framework integrates Internet of Things (IoT) sensors, cloud-based data processing, and machine learning-assisted analytics to continuously monitor critical transformer parameters such as temperature, oil quality, load variations, dissolved gas concentration, vibration, and humidity levels. Sensor data collected from the transformer environment are transmitted through secure communication networks to a centralized monitoring platform for intelligent analysis and anomaly detection. Predictive diagnostic algorithms are employed to identify potential failures and estimate transformer health conditions before critical faults occur. The framework also incorporates automated alert generation and remote monitoring capabilities to improve maintenance planning and operational efficiency. Performance evaluation is conducted using parameters including fault detection accuracy, response time, data transmission reliability, predictive efficiency, and maintenance cost reduction. Experimental analysis demonstrates that the proposed system significantly enhances transformer fault prediction capability, minimizes downtime, improves asset reliability, and supports proactive maintenance strategies compared with traditional monitoring techniques. The results further indicate improved diagnostic accuracy and faster decision-making through continuous real-time monitoring and intelligent data analytics.