Siya Trivedi
Independent Researcher
India
Abstract
This manuscript presents a comparative analysis of five prominent maximum power point tracking (MPPT) algorithms—Perturb and Observe (P&O), Incremental Conductance (IncCond), Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN)—applied to photovoltaic (PV) systems. A MATLAB/Simulink model of a 100 kW grid-connected PV array under varying irradiance and temperature profiles was used. Statistical analysis of tracking efficiency, convergence time, and steady-state oscillations was conducted. Results indicate PSO and ANN achieve higher mean efficiencies (98.1% and 97.9%) with longer convergence times, while P&O and IncCond offer faster response but lower efficiencies. FLC provides a balanced performance. Identified research gaps include real-time hardware validation, dynamic irradiance adaptation without lookup tables, and hybrid algorithm development. Ten references up to 2018 are cited.
Keywords
MPPT, photovoltaic systems, comparative analysis, P&O, IncCond, FLC, PSO, ANN
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