Intelligent adaptive PSO and linear active disturbance rejection control: A novel reinitialization strategy for partially shaded photovoltaic-powered battery charging

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dc.authoridDAGAL, IDRISS/0000-0002-2073-8956
dc.authoridM. Hussein Farh, Hassan/0000-0002-5524-5887
dc.contributor.authorIbrahim, AL-Wesabi
dc.contributor.authorXu, Jiazhu
dc.contributor.authorAl-Shamma'a, Abdullrahman A.
dc.contributor.authorFarh, Hassan M. Hussein
dc.contributor.authorDagal, Idriss
dc.date.accessioned2025-03-09T10:49:03Z
dc.date.available2025-03-09T10:49:03Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis study aims at enhancing the performance of Conventional Particle Swarm Optimizer (CPSO) to maximize the efficiency of solar photovoltaic-powered battery chargers under partial shading (PS) conditions, making it appropriate for several applications such as wheelchairs, and electric vehicles. To address this issue, The Adaptive Particle Swarm Optimizer (APSO) strategy is used in this work. It selects weight factors based on adaptive inertia, uses reinitialization techniques to find variant global maximum peak (GMP), and modifies learning factors to maximize the speed and accuracy of CPSO convergence. The reference voltage output from APSO was then tracked using the Linear Active Disturbance Rejection Control (LADRC). The LADRC improves the maximum power point tracking (MPPT) technique's anti-interference capability against multiple external disturbances and speeds up the system's response time. The simulation and experiment findings demonstrate that hybrid APSO-LADRC overcomes the drawbacks of CPSO, offering better performance for tracking GMP effectively with less oscillations and shorter convergence time compared to CPSO, grasshopper optimization (GHO), incremental conductance (INC) and perturb & observe (P&O) during dynamic and static PS. The results demonstrate that the proposed APSOLADRC's efficiency reaches a level greater than 99 % even in PS, and it additionally demonstrates a faster rate of convergence and less oscillations in power during the entire tracking procedure. Furthermore, the PV system's robust performance highlights its capability to reliably charge batteries in applications like wheelchairs and electric vehicles, ensuring uninterrupted operation and energy efficiency even in challenging shading scenarios.
dc.description.sponsorshipKing Salman center For Disability Research [KSRG-2024-056]
dc.description.sponsorshipThe authors extend their appreciation to the King Salman center For Disability Research for funding this work through Research Group no KSRG-2024-056.
dc.identifier.doi10.1016/j.compeleceng.2024.110037
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85213859636
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2024.110037
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4713
dc.identifier.volume123
dc.identifier.wosWOS:001400292600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofComputers & Electrical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250310
dc.subjectPhotovoltaic
dc.subjectBattery charger
dc.subjectAdaptive particle swarm
dc.subjectPartial shading
dc.subjectWheelchairs
dc.subjectStatic and dynamic environments
dc.titleIntelligent adaptive PSO and linear active disturbance rejection control: A novel reinitialization strategy for partially shaded photovoltaic-powered battery charging
dc.typeArticle

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