Optimal parameters selection of particle swarm optimization based global maximum power point tracking of partially shaded PV Article Swipe
Sergey Obukhov
,
Аhmed Ibrahim
,
Raef Aboelsaud
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1399/2/022032
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1399/2/022032
This paper presents optimal parameters selection of particle swarm optimization (PSO) algorithm for determining the global maximum power point tracking of photovoltaic array under partially shaded conditions. Under partial shading, the power-voltage characteristics have a more complex shape with several local peaks and one global peak. The two proposed controllers include dynamic Particle Swarm Optimization, and constant particle swarm optimization. The developed algorithms are implemented in MATLAB/Simulink platform, and their performances are evaluated. The results indicate that the dynamic particle swarm optimization algorithm can very fast track the GMPP within 128 ms for different shading conditions. In addition, the average tracking efficiency of the proposed algorithm is higher than 99.89%, which provides good prospects to apply this algorithm in the control search unit for the global maximum power point in stations.
Related Topics
Concepts
Particle swarm optimization
MATLAB
Multi-swarm optimization
Tracking (education)
Control theory (sociology)
Mathematical optimization
Maximum power principle
Maximum power point tracking
Photovoltaic system
Point (geometry)
Power (physics)
Computer science
Swarm behaviour
Global optimization
Selection (genetic algorithm)
Algorithm
Mathematics
Engineering
Artificial intelligence
Physics
Control (management)
Electrical engineering
Psychology
Quantum mechanics
Pedagogy
Geometry
Operating system
Inverter
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1399/2/022032
- https://iopscience.iop.org/article/10.1088/1742-6596/1399/2/022032/pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2993739212
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2993739212Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/1399/2/022032Digital Object Identifier
- Title
-
Optimal parameters selection of particle swarm optimization based global maximum power point tracking of partially shaded PVWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-01Full publication date if available
- Authors
-
Sergey Obukhov, Аhmed Ibrahim, Raef AboelsaudList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/1399/2/022032Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/1399/2/022032/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/1399/2/022032/pdfDirect OA link when available
- Concepts
-
Particle swarm optimization, MATLAB, Multi-swarm optimization, Tracking (education), Control theory (sociology), Mathematical optimization, Maximum power principle, Maximum power point tracking, Photovoltaic system, Point (geometry), Power (physics), Computer science, Swarm behaviour, Global optimization, Selection (genetic algorithm), Algorithm, Mathematics, Engineering, Artificial intelligence, Physics, Control (management), Electrical engineering, Psychology, Quantum mechanics, Pedagogy, Geometry, Operating system, InverterTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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