Enhancement of Maximum Power Point Traking of Solar Energy Conversion Using a Newly Designed High-Sensitive Fuzzy PID Controller

Improvement of the maximum power point tracking (MPPT) system for solar chargers and rectifiers remains challenging. We propose a novel parametric design of high-sensitive fuzzy (HSF) proportional-integral-derivative controller (PIDC) for efficient functioning of the MPPT system. This design is based on a synergistic combination of the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic (SFL) schemes. The best parameters of MPPT and PIDC are determined via optimization, where RBF-NN is tuned using GA to achieve the optimal solution. Furthermore, RBF-NN is used to enhance the PID parameters (obtained from GA) for designing HSFL-PIDC of the MPPT system. The entire scheme is further tuned by solar parameters under various operating conditions to improve the solar performance in terms of charging and rectifying. The performance of the proposed analog-implemented MPPT controller is evaluated by interfacing it with a hardware prototype of dual photovoltaic (PV) system. The achieved system is demonstrated to be efficient and robust in improving solar charging and rectifying capacity.