Open Access iconOpen Access

ARTICLE

Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems

Belqasem Aljafari1, Eydhah Almatrafi2,3,4, Sudhakar Babu Thanikanti5, Sara A. Ibrahim6, Mohamed A. Enany6,*, Marwa M. Ahmed7

1 Electrical Engineering Department, College of Engineering, Najran University, Najran, 11001, Saudi Arabia
2 Mechanical Engineering Department, College of Engineering-Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia
3 K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Center of Excellence in Desalination Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India
6 Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt
7 Electrical Engineering Department, Faculty of Engineering, King Abdul-Aziz University, Jeddah, 80204, Saudi Arabia

* Corresponding Author: Mohamed A. Enany. Email: email

Computers, Materials & Continua 2022, 73(2), 3595-3611. https://doi.org/10.32604/cmc.2022.030733

Abstract

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking (MPPT) techniques for Photo-Voltaic based Battery Storage Systems (PV-BSS). To have a full comparative study in terms of the dynamic response, battery state of charge (SOC), and oscillations around the Maximum Power Point (MPP) of the PV-BSS to variations in climate conditions, these techniques are simulated in Matlab/Simulink. The introduced methodologies are classified into two types; the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb & Observe and Incremental Conductance techniques. The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre- build Adaptive Neuro-Fuzzy Inference System (ANFIS) model to predict DC–DC converter optimum duty cycle to track MPP. Then evaluation study is introduced for conventional and proposed On-Line MPPT techniques. This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PV-BSS. Also the introduced model can be used as a valued reference model for future research related to Soft Computing (SC) MPPT techniques. A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

Keywords

Photo-voltaic based battery storage systems; adaptive neuro-fuzzy inference system; maximum power point tracking; perturb & observe technique; incremental conductance technique; state of charge

Cite This Article

APA Style
Aljafari, B., Almatrafi, E., Thanikanti, S.B., Ibrahim, S.A., Enany, M.A. et al. (2022). Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems. Computers, Materials & Continua, 73(2), 3595–3611. https://doi.org/10.32604/cmc.2022.030733
Vancouver Style
Aljafari B, Almatrafi E, Thanikanti SB, Ibrahim SA, Enany MA, Ahmed MM. Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems. Comput Mater Contin. 2022;73(2):3595–3611. https://doi.org/10.32604/cmc.2022.030733
IEEE Style
B. Aljafari, E. Almatrafi, S. B. Thanikanti, S. A. Ibrahim, M. A. Enany, and M. M. Ahmed, “Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems,” Comput. Mater. Contin., vol. 73, no. 2, pp. 3595–3611, 2022. https://doi.org/10.32604/cmc.2022.030733



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1481

    View

  • 843

    Download

  • 0

    Like

Share Link