Comparative Analysis of Classical and Intelligent Power Point Techniques for Solar Photovoltaic Systems

Solar photovoltaic (PV) technology has become a cornerstone of renewable energy systems, but its efficiency is highly dependent on effective Maximum Power Point Tracking (MPPT). This paper compares traditional MPPT methods—such as Perturb and Observe (P&O) and Incremental Conductance (INC)—with intelligent approaches including Fuzzy Logic, Artificial Neural Networks (ANN), and Particle Swarm Optimization (PSO). The analysis focuses on efficiency, response speed, stability, and computational requirements under varying environmental conditions. Findings suggest that while classical methods remain attractive for their simplicity and low cost, intelligent techniques deliver superior accuracy and adaptability, particularly in dynamic operating environments. The study concludes that hybrid strategies may offer the most practical balance between performance and implementation complexity.