Abstract
The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANNFuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VMPP. ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. For energy storage, we consider the utilization of lithiumion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.
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