Autonomous Microgrid Management System with AI-Powered Load and Fault Prediction

Abstract

Intelligent monitoring and control of modern microgrids have become essential to increase the efficiency, reliability and fault tolerance of the microgrid. This research work proposes an autonomous microgrid management system with AI-powered load and fault prediction that combines renewable energy sources, IoT and cloud computing in one embedded system. The proposed system encompasses an ESP32 microcontroller that collects the values of voltage and current using sensors, calculates electrical parameters, and transfers data to the cloud database (Firebase) via Wi-Fi connection. A web dashboard is used to provide real-time monitoring of the system, while a machine learning algorithm is used to predict future load and detect abnormal states for proactive fault management. An automatic relay-based load control scheme is applied to protect the system from abnormal load faults. Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms. The proposed AI prediction model has achieved 95.3% accuracy, proving the feasibility of the proposed system design.