Izvorni znanstveni članak

https://doi.org/10.17559/TV-20250612002740

High-Efficiency MPPT Strategies for Floating and Conventional Solar PV: A Simulation- Based Study

Saad Chayma ; University of Gabes, National Engineering School, Avenue Omar Khattab Gabes 6029, Tunisia
Aymen Flah ; 1) Processes, Energy, Environment, and Electrical Systems, National Engineering School of Gabès, University of Gabès, Tunisia 2) Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan 3) College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia
Habib Kraiem ; Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia *
Ahmad A. Mousa ; Department of Basic Sciences, Middle East University, Amman 11831, Jordan
Claude Ziad El-Bayeh ; Department of Electrical Engineering, Bayeh Institute, Amchit, Lebanon

* Dopisni autor.

Sažetak

Solar photovoltaic (SPV) systems are an environmentally friendly and recyclable source of renewable energy. Direct connection of solar panels to the load results in suboptimal power provision. Therefore, getting the maximum performance from the SPV system is essential to improve efficiency. Various techniques have been proposed to track the maximum power point (MPPT) of the SPV system. Traditional MPPT techniques are usually limited to uniform weather conditions. This paper presents a comprehensive comparative analysis of Maximum Power Point Tracking (MPPT) techniques employed in conventional and floating solar photovoltaic (PV) systems. The study examines various MPPT techniques, including perturb and observe (P&O), particle swarm optimization (PSO), and artificial neural networks (ANN), in both conventional and floating solar photovoltaic systems. The simulations were performed in a MATLAB/Simulink environment. The results of the comparison of MPPT algorithms in this study show that all these algorithms display very high-efficiency rates, generally above 97%, indicating good overall performance of MPPT systems. Still, the ANN and PSO techniques remain at the top. It is also worth noting that FPV systems tend to produce more power than LPV systems, particularly in the summer.

Ključne riječi

artificial neural network; floating photovoltaic system; incremental conductance; land-based system; MPPT techniques; particle swarm optimization; perturb and observe

Hrčak ID:

344996

URI

https://hrcak.srce.hr/344996

Datum izdavanja:

28.2.2026.

Posjeta: 178 *