IoT-Enabled Condition Monitoring of Power Transformers in Distribution Networks

Power transformers are critical assets in distribution networks, where reliable operation ensures uninterrupted power delivery. Condition monitoring through IoT technologies offers continuous data-driven insights into their health. However, existing methods face challenges such as limited real- time accuracy, high maintenance costs, and inefficiencies in early fault detection. To address these issues, this study introduces a Digital Twin-Enhanced IoT Monitoring (DTIM) framework, which integrates digital twin models with IoT sensor data streams for predictive diagnostics. The framework enables real-time simulation of transformer behavior, facilitating early anomaly detection, performance optimization, and proactive maintenance. The proposed method enhances reliability, reduces downtime, and supports intelligent decision-making in distribution networks. Experimental analysis reveals improved fault prediction accuracy and cost-effectiveness compared to traditional monitoring techniques, ensuring sustainable operation of transformers.