Maximizing Power-Point Tracking with Machine Learning

This paper discusses the use of machine learning to track the maximum power point. In order to train neural networks, the error back propagation approach is employed. The advantage of neural networks is their quick and accurate tracking of maximum power points. This method makes use of a neural network to provide the reference voltage for the maximum power point under various atmospheric circumstances. The maximum power point can be tracked by properly controlling the dc-dc boost converter. Simulation results are obtained using MATLAB/SIMULINK to verify theory analysis.