@article{khenissi_Mohamed Amine Fakhfakh_Rafik Neji_2020, title={Artificial Neural Network and Space Vector Pulse Width Modulation Control Technique for a Photovoltaic System with a Power Grid Connection and Lead Acid Battery Storage}, volume={3}, url={https://ijeeas.utem.edu.my/ijeeas/article/view/5788}, abstractNote={<p><em>In order to reduce the pollution generated by fossil fuels, the integration of the </em><em>photovoltaic (PV) system into an electricity grid became a challenge and a solution to overcome </em><em>this problem. However, to avoid the uncertain presence of solar power output as well as the increase </em><em>of load demands during the cloudy day or at night, a storage element which is the battery is needed. </em><em>This paper presents the design and simulation of a grid-connected PV system using MATLAB </em><em>Simulink. Then, it focuses on the Artificial Neural Network (ANN) technique and the Space Vector </em><em>Pulse Width Modulation (SVPWM) method to respectively control the DC/DC Boost converter and </em><em>the main element of all the system which is “the inverter”. Moreover, it gives the design and the </em><em>simulation of a Lead Acid (LA) battery storage connected through a bi-directional (Buck-Boost) </em><em>converter to control its charge and discharging behavior. In fact, simulation results show that PV </em><em>module has successfully integrated into the grid thanks to the efficiency of the ANN and SVPWM </em><em>methods. Then prove that the storage element is able to store energy and provide it when the PV </em><em>power is insufficient based on the charging and discharging process results of the storage battery.</em></p>}, number={1}, journal={International Journal of Electrical Engineering and Applied Sciences (IJEEAS)}, author={khenissi, Imene and Mohamed Amine Fakhfakh and Rafik Neji}, year={2020}, month={Apr.} }