Abstract
Climate dependence requires robust control of the photovoltaic system. The current paper is divided in two main sections: the first part is dedicated to compare and evaluate the behaviors of three different maximum power point tracking (MPPT) techniques applied to photovoltaic energy systems, which are: incremental and conductance (IC), perturb and observe (P&O) and fuzzy logic controller (FLC) based on incremental and conductance. A model of a photovoltaic generator and DC/DC buck converter with different MPPT techniques is simulated and compared using Matlab/Simulink software. The comparison results show that the fuzzy controller is more effective in terms of response time, power loss and disturbances around the operating point. IC and P&O methods are effective but sensitive to high-frequency noise, less stable and present more oscillations around the PPM. In the second section, the FPGA platform is used to implement the proposed control. The FLC architecture is implemented on an FPGA Spartan 3E using the ISE Design Suite software. Simulation results showed the effectiveness of the proposed fuzzy logic controller.
Highlights
The electrical energy produced by photovoltaic systems is very interesting and a modern topic of power generation
The difficulties and the non-linearity of PV systems are influenced by the solar radiation and the temperature conditions, resulting in power losses and reduced efficiency
We developed a photovoltaic generator (PVG) controlled by a fuzzy logic control based on the incremental and the conductance using Matlab/Simulink environment
Summary
The electrical energy produced by photovoltaic systems is very interesting and a modern topic of power generation. In a direct coupled (with no battery storage) PV system, the solar cell arrays are directly connected to the motor load couple These systems are relatively simple and inexpensive to operate. There are different methods of PPM tracking such as IC [4,5,6], parasitic capacitance method [7], P&O method [8], etc They are based on the regulation of the current or voltage of the photovoltaic generator according to climatic conditions. The uncertainties associated with these conditions have decreased the reliability of these techniques, especially as a result of changes in sunlight and temperature Artificial intelligence techniques such as neural networks [9] and fuzzy logic [10, 11] have the characteristics of adaptive and versatile mechanisms and are able to improve the performance of the control system in the presence of these uncertainties
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
