AI-Driven Smart Energy Management and Fault Detection in Grid-Tied Hybrid PV/Wind/Battery Power Generation System

Resumen

This paper presents an AI-driven Smart Energy Management and Fault Detection System for a grid-tied hybrid photovoltaic (PV), wind, and battery power generation system. The proposed model employs a Fuzzy Logic Controller (FLC) for intelligent power flow decisions, optimizing charging, discharging, and grid interaction based on real-time conditions. An ESP32 microcontroller enables data acquisition, control execution, and IoT-based monitoring through platforms like Blynk or ThingSpeak. The integrated AI-based fault detection identifies abnormal PV, wind, or battery behavior, ensuring fault-tolerant operation and improved system reliability. Experimental results confirm enhanced energy efficiency, faster fault response, and stable grid performance. The system offers a scalable and sustainable solution for smart homes, microgrids, and distributed renewable networks.