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Publication . Article . 2019

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

Muhannad Alshareef; Zhengyu Lin; Mingyao Ma; Wenping Cao;
Open Access
English
Published: 15 Feb 2019
Country: United Kingdom
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.

Subjects by Vocabulary

arXiv: Computer Science::Neural and Evolutionary Computation

Library of Congress Subject Headings: lcsh:Technology lcsh:T

Microsoft Academic Graph classification: Control theory Point (geometry) Photovoltaic system Reliability (computer networking) Computer science Tracking (particle physics) Maximum power point tracking Swarm behaviour Maximum power principle

Subjects

MPPT, partial shading conditions, PV, PSO, P&amp, O, MPPT; partial shading conditions; PV; PSO; P&O, Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous), MPPT; partial shading conditions; PV; PSO; P&O

Related Organizations
Funded by
EC| RDC2MT
Project
RDC2MT
Research, Demonstration, and Commercialisation of DC Microgrid Technologies
  • Funder: European Commission (EC)
  • Project Code: 734796
  • Funding stream: H2020 | MSCA-RISE
Validated by funder
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