Comparison Analysis of Maximum Power Point Tracking Between ANN and Cascade ANN

The photovoltaic cell is a system that converts light energy into electrical energy. The system mainly depends on solar irradiance and temperature. Artificial Neural Network-based Maximum Power Point Tracking technique is used in this proposed work. The maximum PowerPoint Tracking system is an electronic device that uses as a controller of the PV solar system to maximize power output. Depending on the surrounding conditions, whether the sky is blue or heavily cloudy, whether it is raining or misting or even overcast, the MPPT system provides the output of the cells and applies the appropriate load to produce maximum power. In this work, using an ANN and cascade artificial neural network for tracking maximum power points is discussed. For the purpose of training neural networks, the feed-forward propagation approach is employed. The neural network has the advantage of fast and precise tracking of maximum PowerPoint. In this methodology, the reference voltage of the maximum power point under different climatic circumstances is specified using a neural network. Maximum power point tracking is possible with suitable boost converter control. The simulation result is produced by utilizing MATLAB/SIMULINK to verify the theory analysis.