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

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dc.citedby34
dc.contributor.authorKoh J.S.en_US
dc.contributor.authorTan R.H.G.en_US
dc.contributor.authorLim W.H.en_US
dc.contributor.authorTan N.M.L.en_US
dc.contributor.authorid58127236400en_US
dc.contributor.authorid35325391900en_US
dc.contributor.authorid57224979685en_US
dc.contributor.authorid24537965000en_US
dc.date.accessioned2024-10-14T03:18:27Z
dc.date.available2024-10-14T03:18:27Z
dc.date.issued2023
dc.description.abstractParticle 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TSTE.2023.3250710
dc.identifier.epage1834
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85149377022
dc.identifier.spage1822
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85149377022&doi=10.1109%2fTSTE.2023.3250710&partnerID=40&md5=6847bfa2326de184310973d980695f65
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34211
dc.identifier.volume14
dc.pagecount12
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Sustainable Energy
dc.subjectMaximum power point tracking
dc.subjectpartial shading
dc.subjectparticle swarm optimization
dc.subjectperturb and observe
dc.subjectLocal search (optimization)
dc.subjectParticle swarm optimization (PSO)
dc.subjectConvergence
dc.subjectLocal search
dc.subjectMaximum Power Point Tracking
dc.subjectPartial shading
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectPartitioning algorithms
dc.subjectPerturb and observe
dc.subjectSearch method
dc.subjectSwarm optimization
dc.subjectMaximum power point trackers
dc.titleA Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Conditionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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