Vidyut: The Conceptual Development of a Smart Gridbased System Utilizing the Arduino Micro-Controller with the Help of Edge Computing in Machine Learning for Fault Detection Process

This research paper introduces a compact self-healing grid system that enhances power distribution network resilience through automated fault detection, power optimization, and predictive analysis capabilities. Utilizing Arduino for grid management, the system features an LCD for displaying real-time grid status, switches to simulate faults, and a buzzer for audible alerts. Additionally, it incorporates voltage and current sensors that continuously monitor the grid's power conditions, ensuring immediate detection and response to anomalies. In the event of a fault, the system promptly detects the issue and initiates a rerouting protocol to maintain uninterrupted power supply. Concurrently, it employs a NodeMCU (ESP8266) module to send detailed real-time alerts via Telegram messaging, providing grid operators with critical voltage, current, and power data. This immediate notification allows for swift action to mitigate any potential disruption caused by the fault. Enhancing its functionality, the system integrates machine learning (ML) technologies to analyze the collected data for predictive maintenance and advanced fault detection. This ML integration empowers grid operators to not only react to faults more effectively but also proactively anticipate and prevent potential failures. The predictive capability provided by ML analyses contributes to fewer outages and enhances the overall reliability of the power distribution network. Overall, the self-healing grid system represents a significant advancement in grid automation and management. By merging real-time monitoring with automatic rerouting and predictive ML-driven technologies, the system significantly improves the efficiency and reliability of power distribution networks. This scalable solution offers a forward-thinking approach to managing grid stability and efficiency, paving the way for more resilient energy infrastructures …. (this is a KSCST awarded student project under the SPP scheme of the Karnataka State Government).