Implementation of MPPT Technique of Solar Module with Supervised Machine Learning
This work addresses efficiency and stability issues in solar energy systems for renewable energy applications, representing an incremental improvement over existing methods.
The authors tackled the problem of efficiently tracking the maximum power point in solar photovoltaic systems by proposing an improved neural network-based MPPT algorithm, achieving higher efficiency compared to the Perturb and Observe method with reduced oscillation and misjudgment, as verified through hardware implementation.
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.