Baatiah, A.O.; Eltamaly, A.M.; Alotaibi, M.A. Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction. Energies2023, 16, 6433.
Baatiah, A.O.; Eltamaly, A.M.; Alotaibi, M.A. Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction. Energies 2023, 16, 6433.
Baatiah, A.O.; Eltamaly, A.M.; Alotaibi, M.A. Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction. Energies2023, 16, 6433.
Baatiah, A.O.; Eltamaly, A.M.; Alotaibi, M.A. Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction. Energies 2023, 16, 6433.
Abstract
Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) methods. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variation of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid this limitation inherent in the conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit to identify the global maximum power point (GMPP) even under fast fluctuation of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The novel adaptive PSO algorithm also opens new possibilities for further innovations in optimization-driven applications, extending its potential impact on a diverse range of real-world challenges. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority.
Keywords
global maximum power; partially shaded PV; particle swarm optimization; MPPT
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
