Vol.73, No.2, 2022, pp.3595-3611, doi:10.32604/cmc.2022.030733
OPEN 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:
Received 31 March 2022; Accepted 10 May 2022; Issue published 16 June 2022
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
B. Aljafari, E. Almatrafi, S. Babu Thanikanti, S. A. Ibrahim, M. A. Enany et al., "Evaluation of on-line mppt algorithms for pv-based battery storage systems," Computers, Materials & Continua, vol. 73, no.2, pp. 3595–3611, 2022.
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