Title: Performance analysis of the MPPT methods in offshore floating and onshore solar systems

Authors: Chayma Saad; Aymen Flah; Habib Kraiem; Ahmad A. Mousa

Addresses: National School of Engineering of Gabes, University of Gabes, Gabès, 6072, Tunisia ' National Engineering School of Gabès, University of Gabès, Gabès, 6072, Tunisia; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan ' Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia ' Department of Basic Sciences, Middle East University, Amman, 11831, Jordan

Abstract: Solar photovoltaic (SPV) systems are an environmentally friendly and recyclable renewable energy source. Direct connection of solar panels to the load results in suboptimal power provision. Therefore, it is important to get the maximum performance from the SPV system 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 different MPPT techniques: perturb and observe (P&O), particle swarm optimisation (PSO) and artificial neural networks (ANN) in both conventional and floating solar photovoltaic systems. The simulations were performed in 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, but the ANN and PSO techniques remain at the top. It can also be noted that FPV systems tend to produce more power than CPV systems, especially in the summer. This may be due to the natural cooling provided by water for FPV systems.

Keywords: FPV system; CPV system; MPPT techniques; perturb and observe; particle swarm optimisation; PSO; artificial neural network; ANN.

DOI: 10.1504/IJPT.2026.154610

International Journal of Powertrains, 2026 Vol.15 No.2, pp.156 - 172

Received: 24 Nov 2024
Accepted: 09 Apr 2025
Published online: 07 Jul 2026 *

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