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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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 |
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DRNTU::Engineering::Electrical and electronic engineering Soon, Jing Jun. Low, Kay-Soon. Photovoltaic model identification using particle swarm optimization with inverse barrier constraint |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Soon, Jing Jun. Low, Kay-Soon. |
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Article |
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Soon, Jing Jun. Low, Kay-Soon. |
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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 |
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Photovoltaic model identification using particle swarm optimization with inverse barrier constraint |
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Photovoltaic model identification using particle swarm optimization with inverse barrier constraint |
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photovoltaic model identification using particle swarm optimization with inverse barrier constraint |
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2013 |
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https://hdl.handle.net/10356/84796 http://hdl.handle.net/10220/13498 |
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