Efficient FPGA Implementation to Estimate the Maximum Output Power of a Photovoltaic Panel Using Xilinx System Generator

Rezki Tadrist,Ali Nesba,Mountassar Maamoun,Adnane Hassani

doi:10.3844/ajassp.2016.522.532

Abstract

This paper presents an efficient architecture for different mathematical models for optimal design of a photovoltaic system using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB/Simulink and XSG and explores important aspects of the experimental implementation. The use of the Xilinx generator for calculating the power output of a photovoltaic system reduces the complexity and structural design and also provides an additional feature for materializing the system.

Highlights

Nowadays, designing an optimized photovoltaic system remains a challenge facing researchers
The output power of PV generator is strongly correlated to the temperature, to the light and to the photovoltaic panel ageing
Simulation results of the models 1, 2, 3 and 4 are presented in Fig. 2 for four irradiance values (300, 500, 700 and 1000 W/m2). It is seen from the simulation results that there is a proportional change in the output power of the PV module depending on the sunlight

Summary

Introduction

Nowadays, designing an optimized photovoltaic system remains a challenge facing researchers. The output power of PV generator is strongly correlated to the temperature, to the light and to the photovoltaic panel ageing. The problem of perfect coupling between the PV generator and the load is not yet fully resolved. The problem of working at the maximum power point of the PV Generator (PVG) is not practically achieved despite many control algorithms devoted to this task. There are two approaches for modeling the solar panels. The first requires to perform measurements on the solar panel when installed. This case corresponds to the models developed by Sandia and Cenerge (Chenni et al, 2007). The second approach is to focus only on the manufacturer's data (King et al, 1998)

Methods

Results

Conclusion