Neural Network-Enhanced MPPT for Hybrid PV-Wind-Battery DC Micro-grid System

In the pursuit of sustainable energy solutions, the incorporation of photovoltaic (PV) and wind energy systems has gained substantial attention. This paper presents a comprehensive study on a PV Wind Battery-Based DC Micro-grid (MG), optimized using a Neural Network-based Maximum Power Point Tracking (MPPT) algorithm. The proposed MG system aims to enhance energy efficiency and reliability by leveraging the complementary nature of PV and wind power sources, coupled with battery storage for energy management. The neural network MPPT algorithm is designed to dynamically adjust to varying environmental conditions, ensuring optimal power extraction from both PV and wind systems. The effectiveness of the proposed system is validated through extensive simulations results, demonstrating significant improvements in power conversion efficiency and stability, it achieves an efficiency of 99.7%. This research underscores the potential of advanced AI techniques in optimizing hybrid renewable energy systems, paving the way for more resilient and efficient MG solutions.