Artificial Neural Network and Space Vector Pulse Width Modulation Control Techniques for a Photovoltaic System with a Power Grid Connection and Lead Acid Battery Storage

Authors

  • Imene khenissi

Abstract

In order to reduce the pollution generated by fossil fuels, the integration of the photovoltaic (PV) system into an electricity grid became a challenge and a solution to overcome this problem. However, to avoid the uncertain presence of solar power output as well as the increase of load demands during the cloudy day or at night, a storage element which is the battery is needed.

This paper presents the design and simulation of a grid-connected PV system using MATLAB Simulink and focuses on the Artificial Neural Network MPPT technique to control the DC/DC Boost converter and the Space Vector Pulse Width Modulation (SVPWM) method used to control the main element of all the system which is “the inverter”. Then, it gives the design and the simulation of a Lead Acid battery storage connected through a bi-directional (Buck-Boost) converter to control its charge and discharging behavior. In fact, simulation results show that PV module has successfully integrated into the grid thanks to the efficiency of the ANN and SVPWM methods. Then prove that the storage element is able to store energy and provide it when the PV power is insufficient based on the charging and discharging process results of the storage battery.

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Published

2020-04-29

How to Cite

khenissi, I. (2020). Artificial Neural Network and Space Vector Pulse Width Modulation Control Techniques for a Photovoltaic System with a Power Grid Connection and Lead Acid Battery Storage. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 3(1), 43–52. Retrieved from https://ijeeas.utem.edu.my/ijeeas/article/view/5788