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

Muhannad Alshareef; Zhengyu Lin; Mingyao Ma; Wenping Cao

doi:10.3390/en12040623

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

Muhannad Alshareef, Zhengyu Lin, Mingyao Ma, Wenping Cao

College of Engineering and Physical Sciences

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	623
Journal	Energies
Volume	12
Issue number	4
DOIs	https://doi.org/10.3390/en12040623
Publication status	Published - 15 Feb 2019

Bibliographical note

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Funding: This research has received scholarship from Saudi Arabia Cultural Bureau in the UK and funding from
the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 734796

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10.3390/en12040623Licence: CC BY 3.0

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Funding: This research has received scholarship from Saudi Arabia Cultural Bureau in the UK and funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 734796
Final published version, 10.9 MBLicence: CC BY 3.0

Cite this

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title = "Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions",

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.",

author = "Muhannad Alshareef and Zhengyu Lin and Mingyao Ma and Wenping Cao",

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AU - Alshareef, Muhannad

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AU - Cao, Wenping

N1 - © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Funding: This research has received scholarship from Saudi Arabia Cultural Bureau in the UK and funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 734796

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AB - 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.

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

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