Artificial intelligence (AI) techniques have been widely used for maximum power point tracking (MPPT) in solar power systems over the past decade. Conventional MPPT algorithms often fail to accurately track the maximum power point (MPP) under rapidly changing environmental conditions, necessitating the integration of AI. Each AI-based MPPT technique has its own pros and cons, making the selection process challenging. Compared to conventional methods, AI-based techniques offer significant improvements in tracking accuracy, response time, and adaptability, especially under varying irradiance and temperature conditions. This paper provides a comprehensive overview of AI-based MPPT algorithms for solar power systems, exploring methodologies such as neural networks, fuzzy logic, genetic algorithms, and reinforcement learning. The principles, advantages, and implementation challenges of these techniques are highlighted. A comparative analysis with traditional MPPT techniques is presented to emphasize the enhancements brought by AI integration. While AI-based methods offer quick convergence speeds, low steady-state oscillation, and high efficiency, they also require substantial computational power and higher implementation costs. Hybrid MPPT methods, which combine the strengths of both conventional and AI-based techniques, offer a balanced approach in terms of performance and complexity. This study includes a detailed comparison and classification of six main AI-based MPPT approaches based on MATLAB/Simulink simulation results. It assesses the technological implementations and unresolved issues of AI-based MPPT techniques, providing fresh perspectives on selecting the most suitable methods. By leveraging AI, solar energy systems can achieve higher efficiency and reliability, significantly contributing to sustainable energy solutions.

Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).