A Compact Self-Healing Grid System using Arduino and Machine Learning for Fault Management

This research paper 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 real-time 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.