Photovoltaic model identification using particle swarm optimization with inverse barrier constraint

The photovoltaic (PV) model is used in simulation studies to validate system design such as the maximum power point tracking algorithm and microgrid system. It is often difficult to simulate a PV module characteristic under different environmental conditions due to the limited information provided b...

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Main Authors: Soon, Jing Jun., Low, Kay-Soon.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/84796
http://hdl.handle.net/10220/13498
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-847962020-03-07T13:57:29Z Photovoltaic model identification using particle swarm optimization with inverse barrier constraint Soon, Jing Jun. Low, Kay-Soon. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The photovoltaic (PV) model is used in simulation studies to validate system design such as the maximum power point tracking algorithm and microgrid system. It is often difficult to simulate a PV module characteristic under different environmental conditions due to the limited information provided by the manufacturers. In this paper, a new approach using particle swarm optimization (PSO) with inverse barrier constraint is proposed to determine the unknown PV model parameters. The proposed method has been validated with three different PV technologies and the results show that the maximum mean modeling error at maximum power point is less than 0.02% for Pmp and 0.3% for Vmp. 2013-09-16T08:15:08Z 2019-12-06T15:51:15Z 2013-09-16T08:15:08Z 2019-12-06T15:51:15Z 2012 2012 Journal Article Soon, J. J., & Low, K.-S. (2012). Photovoltaic Model Identification Using Particle Swarm Optimization With Inverse Barrier Constraint. IEEE Transactions on Power Electronics, 27(9), 3975-3983. 0885-8993 https://hdl.handle.net/10356/84796 http://hdl.handle.net/10220/13498 10.1109/TPEL.2012.2188818 en IEEE transactions on power electronics © 2012 IEEE
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Soon, Jing Jun.
Low, Kay-Soon.
Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
description The photovoltaic (PV) model is used in simulation studies to validate system design such as the maximum power point tracking algorithm and microgrid system. It is often difficult to simulate a PV module characteristic under different environmental conditions due to the limited information provided by the manufacturers. In this paper, a new approach using particle swarm optimization (PSO) with inverse barrier constraint is proposed to determine the unknown PV model parameters. The proposed method has been validated with three different PV technologies and the results show that the maximum mean modeling error at maximum power point is less than 0.02% for Pmp and 0.3% for Vmp.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Soon, Jing Jun.
Low, Kay-Soon.
format Article
author Soon, Jing Jun.
Low, Kay-Soon.
author_sort Soon, Jing Jun.
title Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
title_short Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
title_full Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
title_fullStr Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
title_full_unstemmed Photovoltaic model identification using particle swarm optimization with inverse barrier constraint
title_sort photovoltaic model identification using particle swarm optimization with inverse barrier constraint
publishDate 2013
url https://hdl.handle.net/10356/84796
http://hdl.handle.net/10220/13498
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