Implementation of MPPT Technique of Solar Module with Supervised Machine Learning

TL;DR

This paper introduces a neural network-based MPPT algorithm for solar PV systems that improves efficiency and reduces power loss compared to traditional methods, validated through simulation and hardware implementation.

Contribution

The paper presents a novel neural network-based MPPT method using Bayesian Regularization, outperforming traditional techniques in accuracy and efficiency.

Findings

Higher efficiency than Perturb and Observe method

Reduced power loss around MPP

Stable operation without oscillation

Abstract

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.