IoT-driven predictive analytics for transformer health monitoring

The integration of IoT technologies provides encouraging possibility for real-time monitoring and fault detection in crucial components like power transformers. The proposed model deals with the transformer parameter metrics in real time, comprising of temperature, voltage, current, and gas content. Data obtained from sensors is wirelessly transmitted to a cloud server for remote access and predictive maintenance. This aims to improve the reliability and life-time of the transformers by allowing proactive fault detection. The proposed architecture is designed for scalability and integration with modern smart grid infrastructures. Experimental results show that the proposed system effectively detects the faults at a very early stage reducing the maintenance cost and avoiding catastrophic failures.