Abstract
This paper presents an Intelligent controller designed to mastery the output power flow from the Solar System, the Wind system, the sum of the two systems or from the battery system, according to the Maximum power point tracking algorithm, to ensure the continuity of the output power at fast time response. The proposed controller has been designed using MATLAB m-file and trained with the different number of hidden neurons using two different algorithms to get as fast a response time with minimum Mean Square Error (MSE) as possible which resulted in six hidden neurons using Levenberg-Marquardt training algorithms.
Highlights
The use of renewable energy sources is growing globally, which leads to a variety of sustainable energy, reduces emissions of greenhouse gases, and becomes less dependent on oil[1]
According to the weather in Iraq and the long hours of solar irradiation as we proposed previously the Solar System will be the main source the Wind System, the Sum, and the Battery System, the Artificial Neural Network will decide the suitable output
MATLAB/Simulink is the best program to simulate the solar-wind energy system and testing the algorithms to verify building the system and avoiding any modeling problems that might appear in the hardware model
Summary
The use of renewable energy sources is growing globally, which leads to a variety of sustainable energy, reduces emissions of greenhouse gases, and becomes less dependent on oil[1]. Solar and wind energy are environment-friendly everlasting sources of energy available all over the earth. It an alternative electricity source especially with the consumption of petroleum, natural gas, and other nonrenewable sources [2]. Solar energy is commonly used because it is pollution-free and transforms incident sunlight directly into electrical energy by using solar cells[4]. Solar cells depend on a variety of factors, 326 Publishing rights belongs to University of Technology’s Press, Baghdad, Iraq
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