LoRaWAN-Based Smart Transformer Monitoring and Control Using Machine Learning for Predictive Maintenance

Abstract

Effective monitoring and control of power transformers are crucial to maintaining the reliability and stability of electricity grids. This paper introduces a LoRaWAN-based transformer control and monitoring system with machine learning (ML) algorithms integrated for predictive maintenance and fault detection. The system utilizes low-power, long-range (LoRa) communication to transfer real-time transformer parameters such as voltage, current, temperature, oil level, humidity, and vibration to a cloud-based server. A Random Forest algorithm is applied to sensor data analysis, anomaly detection, and prognostics of probable failures at high accuracy levels. Machine learning is used to implement condition-based maintenance, which lowers downtime, operating expenses, and unforeseen transformer failure by a great extent. The system also includes a secure and scalable architecture that provides real-time data processing and remote control support, making it applicable to both urban and rural substations. Experimental results show the capability of the system to effectively perform early fault detection than traditional monitoring methods, resulting in improved grid resilience, operation efficiency, and cost savings. This novel solution helps the creation of a smart, data-infused power grid with overall system safety and reliability enhanced.