Photovoltaic systems : analytic comparison of fuzzy logic and ML methods for applying maximum power tracking systems

Abstract

Integration of artificial intelligence (AI) in solar power systems for maximum power point tracking (MPPT) is increasingly popular due to the limitations of traditional MPPT methods in locating the global maximum power point (GMPP) under partial shading conditions. Unlike conventional techniques, AI-based algorithms excel at identifying the GMPP even when multiple local maximum power points (MPPs) exist. Compared to traditional methods, AI-based MPPT techniques like reinforcement learning and fuzzy logic typically offer higher efficiency, reduced steady-state oscillation, and faster convergence but require significant resources and investment. This paper compares two AI-based MPPT methods-Fuzzy Logic and Reinforcement Learning using simulation. Each AI approached its strengths and weaknesses, complicating on optimal method selection. It provided a detailed efficiency comparison of these AI methods by implementing them in a solar power grid system under various environmental conditions. © 2025 IEEE