Optimization of Solar Energy Using Artificial Neural Network Controller

The use of solar panels has some advantages over other conventional electrical generating methods, such as there is no sound pollution in collecting solar energy via solar panels, and it has a minimum need for maintenance. In addition, it helps in the greenhouse effect which does not contribute to any CO
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pollution. Using photovoltaic (PV) systems that are connected to a load will require a Maximum Power Point Tracker (MPPT) to maintain the highest possible efficiency of power generated. Due to the difference in PV and load resistance, the MPPT will control the duty cycle of the Insulated Gate Bipolar Transistor (IGBT) in the DC-DC converter to match the PV and load resistance. However, the use of MPPT will require a controller to seek the maximum power generated in the PV system. In this paper, we design and implement the Artificial Neural Network (ANN) based MPPT method to improve the efficiency of the power observation for the PV system. Mainly, we compare three sets of data to train the ANN controller, 104 samples, 10001 samples, and 2000001 samples, as ANN uses prediction in its calculations to find the best output efficiency.