Maximum power point tracking of photovoltaic system using artificial neural networks

As technological developments progress, the need for energy increases. In order to meet this, fossil fuels are consumed intensively. In this case, the damage of fossil fuels to the environment and living things is growing. Due to the negative environmental factors caused by fossil resources and because these resources will be exhausted one day, a tendency towards renewable energy sources has begun. Photovoltaic systems, which convert solar energy into electricity, are becoming increasingly popular. It reduces the need for photovoltaic systems environmental conditions, and these systems can be less expensive. It can be scaled up to get through the night by harnessing the power of photovoltaic systems. We use maximum power point tracking techniques in this. For photovoltaic systems, the maximum power point tracking method is available. This feat has been meticulously researched and employs artificial neural networks as the MPPT method most appropriate for the system. In the MATLAB/Simulink environment, a photovoltaic panel, a DC/DC boost converter, a control unit, and a resistive load are designed. The trained artificial neural network selects the maximum power point that best meets its temperature requirements as well as the best step-up converter for solar radiation. The outcomes of simulation The performance of artificial neural network based on maximum power point tracking control is effective in achieving PV method maximum power point at various temperatures and irradiance levels.