Fuzzy Logic MPP Tracker for Photovoltaic Systems

This program employs fuzzy logic, a mathematical system that processes degrees of truth rather than conventional binary Boolean logic, to track the Maximum Power Point (MPP) of photovoltaic systems. The implementation utilizes fuzzy inference systems with membership functions and rule bases that process input variables such as voltage-current characteristics and environmental parameters. Through real-time analysis of power derivatives and error signals, the algorithm dynamically adjusts the operating point to maintain optimal power extraction from solar panels under varying conditions including temperature fluctuations, humidity changes, and partial shading scenarios. The fuzzy logic controller employs Mamdani or Sugeno inference methods with defuzzification techniques to generate precise control signals for power converters. Additional optimization algorithms including perturbation and observation methods work in conjunction with the fuzzy system to enhance tracking efficiency. The program implements these through MATLAB's Fuzzy Logic Toolbox functions (fuzzy, addvar, addmf) and Simulink blocks for system modeling, ensuring maximum operational efficiency and reliable power output. This sophisticated approach provides superior performance compared to traditional MPPT methods, making it an essential tool for optimizing solar energy harvesting and reducing dependence on non-renewable power sources.