DOIONLINE 
DOIONLINE Publish In |
International Journal of Industrial Electronics and Electrical Engineering (IJIEEE)-IJIEEE |
 Journal Home Volume Issue |
||||||||
Issue |
Volume-10,Issue-6 ( Jun, 2022 ) | |||||||||
Paper Title |
Modeling of PV Panel and Investigation of MPPT Methods for Grid-connected PV system | |||||||||
Author Name |
Mohammed Mahmood, Ali Kircay, Selim Borekci | |||||||||
Affilition |
||||||||||
Pages |
5-13 | |||||||||
Abstract |
Abstract - Investigation of the maximum power point tracking approach for solar systems utilizing DC-DC flyback converter controlled by Neural Network for grid-connected PV system presented in this article. MATLAB/ Simulink is used to model and simulate the entire system, and the neural network toolbox is used to train the NN. The outcomes of the proposed MPPT controller are then compared to a P&O MPPT algorithm. The parametersVpv, Ipv, Ppv, and DC link voltage of the PV array also inverter voltage and inverter current are taken into account in this research paper.The modeling and analysis of the mentioned MPPT approaches are simulated on a 10.457-kW PV array that acts as a grid-connected power generator. According to the findings, the recommended NN-based MPPT controller performs better and produces good results in terms of maximum power point (MPP) under a variety of weather conditions. It was also discovered that the intelligent-based MPPT algorithm has lower power rippling, high-speed response, harmonics reduction, and quick outcomes than traditional P&O algorithms. Keywords - PV Panel, DC-DC Flyback Converter, MPPT, P & O Algorithm, Artificial Neural Network (ANN). | |||||||||
| View Paper | ||||||||||
