Self-Healing Smart Grid with Time Fault Detection and Predictive Maintenance of Grids

This project presents a compact self-healing grid system designed to automate fault detection, optimize power distribution, and provide real-time notifications and data for predictive analysis. The system leverages Arduino for grid management, an LCD for real-time display of grid status, switches to simulate faults, and a buzzer for audible fault alarms. Voltage sensors and current sensors continuously monitor the power system, while a NodeMCU (ESP8266) module facilitates the sending of real-time alerts via Telegram messaging. In the event of a fault, the system detects the anomaly, reroutes power to maintain supply, and sends alerts containing detailed voltage, current, and power data. The system is further enhanced by integrating machine learning (ML), which processes this data for predictive maintenance and fault detection, enabling grid operators to anticipate and prevent failures. By combining realtime monitoring, automatic rerouting, and ML-driven predictive capabilities, this self-healing grid system improves both the resilience and efficiency of power distribution networks, offering a scalable solution for grid automation and management. The matter presented in this article is the major project work done by the students under the guidance of the authors.