Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

Muhannad Alshareef, Zhengyu Lin, Mingyao Ma and Wenping Cao
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Muhannad Alshareef: Power Electronics, Machine and Power System Group, Aston University, Birmingham B4 7ET, UK
Zhengyu Lin: Power Electronics, Machine and Power System Group, Aston University, Birmingham B4 7ET, UK
Mingyao Ma: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Wenping Cao: Power Electronics, Machine and Power System Group, Aston University, Birmingham B4 7ET, UK

Energies, 2019, vol. 12, issue 4, 1-18

Abstract: This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

Keywords: MPPT; partial shading conditions; PV; PSO; P&O (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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