A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition

Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading...

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Main Authors: Koh J.S., Tan R.H.G., Lim W.H., Tan N.M.L.
Other Authors: 58127236400
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Published: Institute of Electrical and Electronics Engineers Inc. 2024
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spelling my.uniten.dspace-342112024-10-14T11:18:27Z A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition Koh J.S. Tan R.H.G. Lim W.H. Tan N.M.L. 58127236400 35325391900 57224979685 24537965000 Maximum power point tracking partial shading particle swarm optimization perturb and observe Local search (optimization) Particle swarm optimization (PSO) Convergence Local search Maximum Power Point Tracking Partial shading Particle swarm Particle swarm optimization Partitioning algorithms Perturb and observe Search method Swarm optimization Maximum power point trackers Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading conditions (PSC), leading to poor efficiency and significant power loss. Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. A modified global search method with fewer parameters is devised to rapidly identify approximated location of GMPP. Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. MPSO-HALS is implemented into low-cost microcontroller for real-time application. Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3 s and tracking accuracy above 99% under different complex PSCs. � 2010-2012 IEEE. Final 2024-10-14T03:18:27Z 2024-10-14T03:18:27Z 2023 Article 10.1109/TSTE.2023.3250710 2-s2.0-85149377022 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149377022&doi=10.1109%2fTSTE.2023.3250710&partnerID=40&md5=6847bfa2326de184310973d980695f65 https://irepository.uniten.edu.my/handle/123456789/34211 14 3 1822 1834 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Maximum power point tracking
partial shading
particle swarm optimization
perturb and observe
Local search (optimization)
Particle swarm optimization (PSO)
Convergence
Local search
Maximum Power Point Tracking
Partial shading
Particle swarm
Particle swarm optimization
Partitioning algorithms
Perturb and observe
Search method
Swarm optimization
Maximum power point trackers
spellingShingle Maximum power point tracking
partial shading
particle swarm optimization
perturb and observe
Local search (optimization)
Particle swarm optimization (PSO)
Convergence
Local search
Maximum Power Point Tracking
Partial shading
Particle swarm
Particle swarm optimization
Partitioning algorithms
Perturb and observe
Search method
Swarm optimization
Maximum power point trackers
Koh J.S.
Tan R.H.G.
Lim W.H.
Tan N.M.L.
A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
description Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading conditions (PSC), leading to poor efficiency and significant power loss. Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. A modified global search method with fewer parameters is devised to rapidly identify approximated location of GMPP. Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. MPSO-HALS is implemented into low-cost microcontroller for real-time application. Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3 s and tracking accuracy above 99% under different complex PSCs. � 2010-2012 IEEE.
author2 58127236400
author_facet 58127236400
Koh J.S.
Tan R.H.G.
Lim W.H.
Tan N.M.L.
format Article
author Koh J.S.
Tan R.H.G.
Lim W.H.
Tan N.M.L.
author_sort Koh J.S.
title A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
title_short A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
title_full A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
title_fullStr A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
title_full_unstemmed A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
title_sort modified particle swarm optimization for efficient maximum power point tracking under partial shading condition
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061171640107008
score 13.214268