Machine Learning–Based Maximum Power Point Tracking for Solar PV Systems under Dynamic and Partial Shading Conditions

The Maximum Power Point (MPP), which varies with irradiance, temperature, and shading. Conventional Maximum Power Point Tracking (MPPT) methods such as Perturb & Observe (P&O) and Incremental Conductance (IncCond) are widely used due to their simplicity but often struggle with steady-state oscillations, slow convergence, and poor performance under partial shading. Recent advancements in machine learning (ML) provide new approaches for intelligent and adaptive MPPT control. This paper investigates machine learning–based MPPT methods, comparing their performance with classical techniques. Artificial Neural Networks (ANN), Reinforcement Learning (RL), and hybrid fuzzy-neuro approaches are evaluated in simulation under varying environmental conditions. The results demonstrate that ML-based techniques achieve higher tracking efficiency, faster dynamic response, and better adaptability to partial shading, making them strong candidates for next-generation PV systems.