Abstract
Electricity generation using renewable energy resources rather than fossil fuels reduces gobar gas emissions from the power sector and prevents pollution. The best alternative to conventional energy sources is solar photovoltaic (PV) generation; however, it has some significant disadvantages, including high initial costs, poor photoconversion efficiency, and climatic dependencies. In order to overcome this, PPT techniques are used. The projected work provides a solar power system which tracks the maximum power point using Artificial Neural Network and Perturb and Observe algorithm. Additionally, it adds a new setup for the system that links the photovoltaic array as well as the battery to the system inverter’s DC-link through a single DC-DC converter that can act as both charge controller and Maximum Power Point Tracking (MPPT) device at the same time. When solar energy production exceeds demand, the excess energy is stored in batteries and provided during periods of very low irradiance. The DC voltage is converted into AC voltage using an inverter. The benefit of this technique is that it has greater tracking accuracy. For validation, the proposed model has been analyzed by using MATLAB/Simulink.
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