Intelligent maximum power point trackers for photovoltaic applications using FPGA chip: A comparative study

Abstract

In this paper, various intelligent methods (IMs) used in tracking the maximum power point and their possible implementation into a reconfigurable field programmable gate array (FPGA) platform are presented and compared. The investigated IMs are neural networks (NN), fuzzy logic (FL), genetic algorithm (GA) and hybrid systems (e.g. neuro-fuzzy or ANFIS and fuzzy logic optimized by genetic algorithm). Initially, a complete simulation of the photovoltaic system with intelligent MPP tracking controllers using MATLAB/Simulink environment is given. Secondly, the different steps to design and implement the controllers into the FPGA are presented, and the best controller is tested in real-time co-simulation using FPGA Virtex 5. Finally, a comparative study has been carried out to show the effectiveness of the developed IMs in terms of accuracy, quick response (rapidity), flexibility, power consumption and simplicity of implementation. Results confirm the good tracking efficiency and rapid response of the different IMs under variable air temperature and solar irradiance conditions; however, the FL–GA controller outperforms the other ones. Furthermore, the possibility of implementation of the designed controllers into FPGA is demonstrated.