  In this paper, we proposed a method using supervised ML in solar PV system
for MPPT analysis. For this purpose, an overall schematic diagram of a PV
system is designed and simulated to create a dataset in MATLAB/ Simulink. Thus,
by analyzing the output characteristics of a solar cell, an improved MPPT
algorithm on the basis of neural network (NN) method is put forward to track
the maximum power point (MPP) of solar cell modules. To perform the task,
Bayesian Regularization method was chosen as the training algorithm as it works
best even for smaller data supporting the wide range of the train data set. The
theoretical results show that the improved NN MPPT algorithm has higher
efficiency compared with the Perturb and Observe method in the same
environment, and the PV system can keep working at MPP without oscillation and
probability of any kind of misjudgment. So it can not only reduce misjudgment,
but also avoid power loss around the MPP. Moreover, we implemented the
algorithm in a hardware set-up and verified the theoretical result comparing it
with the empirical data.
