Implementation & Comparative Study of PSO, GSA and ANFIS on MPPT PV System using MATLAB/Simulink

The growing global energy demand has emphasized the need for efficient and sustainable power generation, making solar photovoltaic (PV) systems a key renewable energy solution. PV cells exhibit a nonlinear voltage- current (V-I) characteristic with a Maximum Power Point (MPP) which varies with environmental conditions such as solar irradiance and temperature. Extracting maximum power requires efficient real-time tracking of the MPP, a task addressed by Maximum Power Point Tracking (MPPT) techniques. This paper investigates on implementation and comparative analysis of three advanced MPPT algorithms like Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Adaptive Neuro Fuzzy Inference System (ANFIS) using MATLAB/Simulink. Each algorithm is simulated under different operating conditions to evaluate performance in terms of running speed, power output, stability and tracking efficiency. The Comparison of outputs and performances for each method is concluding by providing a comprehensive comparison. The study concludes by identifying the most effective MPPT technique for improving PV system efficiency, reliability and adaptability, offering valuable insights for designing optimized solar energy systems for real-world applications