Related papers: Implementation of MPPT Technique of Solar Module with Supervised Machine Learning

Implementation of MPPT Technique of Solar Module with Supervised Machine Learning

URL: http://arxiv.org/abs/2110.00728v1
Date: Sat, 2 Oct 2021 05:19:37 GMT
Title: Implementation of MPPT Technique of Solar Module with Supervised Machine Learning
Authors: Ruhi Sharmin, Sayeed Shafayet Chowdhury, Farihal Abedin, and Kazi Mujibur Rahman
Abstract summary: We propose a method using supervised ML in solar PV MPPT analysis. 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.
Score: 0.47248250311484113
License: http://creativecommons.org/licenses/by/4.0/
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.

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