AI-advanced MPPT for optimized hybrid solar-wind energy harvesting in off-grid rural electrification: Fabrication and performance modeling

Hybrid Renewable Energy Systems (HRES), which integrate solar and wind power, offer an effective solution for addressing energy demands in rural, off-grid areas. Despite the abundant availability of solar energy during the day and continuous wind energy, the intermittent nature of these resources presents challenges to system efficiency. Maximum Power Point Tracking (MPPT) techniques are crucial for optimizing energy extraction from photovoltaic (PV) panels and wind turbines, but fluctuating environmental conditions complicate their performance. This study adopted a narrative review approach and 127 related articles on the integration of Artificial Intelligence (AI) in MPPT, focusing on AI-driven algorithms like Artificial Neural Networks (ANNs), Fuzzy Logic Control (FLC), and Reinforcement Learning (RL), which enhance system performance by providing adaptive, predictive, and self-learning capabilities were successfully reviewed. ANNs offer high accuracy by predicting optimal operating points based on historical data, but require extensive datasets and computational resources. FLC manages uncertainty and nonlinearity using fuzzy rules but demands significant computational power and expert knowledge. RL autonomously learns optimal strategies and adapts in real time, though it requires substantial training data and computational resources. The incorporation of these AI techniques into HRES facilitates real-time optimization, improving energy efficiency and ensuring a reliable power supply despite dynamic environmental conditions. Additionally, the practical fabrication of AI-enhanced hybrid systems involves careful selection of solar panels, wind turbines, energy storage solutions, and power electronics, along with the implementation of AI-based MPPT controllers on microcontrollers or embedded processors. Simulation and experimental validation confirm the efficacy of these approaches, showcasing their potential to optimize power extraction and enhance energy reliability in remote applications, paving the way for efficient renewable energy systems in rural and off-grid areas.