An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

S.M. Mirhassani; S.Z.M. Golroodbari; S.M.M. Golroodbari; S. Mekhilef

doi:10.1016/j.ijepes.2014.07.074

An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

S.M. Mirhassani
, S.Z.M. Golroodbari
, S.M.M. Golroodbari
, S. Mekhilef

University of Malaya

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

Abstract

This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. © 2014 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	761-770
Number of pages	10
Journal	International Journal of Electrical Power and Energy Systems
Volume	64
DOIs	https://doi.org/10.1016/j.ijepes.2014.07.074
Publication status	Published - 2015
Externally published	Yes

Bibliographical note

Cited By :57

Export Date: 31 March 2023

Keywords

Maximum power point tracking
Partially shaded condition
Particle swarm optimization
Variable sampling time

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ijepes.2014.07.074

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Cite this

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title = "An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time",

abstract = "This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. {\textcopyright} 2014 Elsevier Ltd. All rights reserved.",

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doi = "10.1016/j.ijepes.2014.07.074",

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T1 - An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

AU - Mirhassani, S.M.

AU - Golroodbari, S.Z.M.

AU - Golroodbari, S.M.M.

AU - Mekhilef, S.

N1 - Cited By :57 Export Date: 31 March 2023

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Y1 - 2015

N2 - This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. © 2014 Elsevier Ltd. All rights reserved.

AB - This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs. © 2014 Elsevier Ltd. All rights reserved.

KW - Maximum power point tracking

KW - Partially shaded condition

KW - Particle swarm optimization

KW - Variable sampling time

U2 - 10.1016/j.ijepes.2014.07.074

DO - 10.1016/j.ijepes.2014.07.074

M3 - Article

SN - 0142-0615

VL - 64

SP - 761

EP - 770

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

ER -

An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

Abstract

Bibliographical note

Keywords

UN SDGs

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