Design and analysis of a novel adaptive learning control scheme for performance promotion of grid-connected PV systems

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dc.authoridOZBEK, Necdet Sinan/0000-0002-7184-9015
dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.contributor.authorOzbek, Necdet Sinan
dc.contributor.authorCelik, Ozgur
dc.date.accessioned2025-01-06T17:43:22Z
dc.date.available2025-01-06T17:43:22Z
dc.date.issued2022
dc.description.abstractThis paper addresses a hybrid adaptive iterative learning control strategy for controlling power converters that are used in photovoltaic systems to enhance maximum power point tracking capability in the presence of variable atmospheric conditions. The adaptation of the controller to the fast-changing environmental conditions is provided by a fractional-order proportional-integral type learning control mechanism. The developed control scheme is integrated into a grid-connected current-source flyback inverter to highlight the improvements in performance criteria such as convergence speed during transients, tracking accuracy, steady-state oscillations, and robustness. The performance analyses are carried out under various scenarios. The obtained results reveal that the dynamic response of the system is considerably increased under erratic atmospheric conditions while steady-state oscillations are decreased for stable operation conditions. The maximum absolute error that indicates the robustness of the proposed controller is decreased from 2.3704 to 2.1920. In addition, the error deviations of the proposed control algorithm are below 10%. The variance of the error, which shows steady-state stability, is reduced from 2.5123 to 1.6152. Also, the proposed controller reduces the amount of control energy by 20% when compared to the PI controller. Furthermore, the values of IAE and ISE are reported 10% lower in the proposed controller.
dc.identifier.doi10.1016/j.seta.2022.102045
dc.identifier.issn2213-1388
dc.identifier.issn2213-1396
dc.identifier.scopus2-s2.0-85124251881
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.seta.2022.102045
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2640
dc.identifier.volume52
dc.identifier.wosWOS:000789645000003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofSustainable Energy Technologies and Assessments
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectIterative learning control
dc.subjectFractional-order control
dc.subjectMPPT
dc.subjectFlyback converters
dc.subjectStability analysis
dc.subjectPV systems
dc.titleDesign and analysis of a novel adaptive learning control scheme for performance promotion of grid-connected PV systems
dc.typeArticle

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