Enhanced grid stability and prolonging life span in renewable energy power converters using an advanced Sugeno-type AI-based neuro-fuzzy control

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dc.contributor.authorÖzden, Mustafa
dc.contributor.authorErtekin, Davut
dc.contributor.authorBaltacı, Kübra
dc.date.accessioned2026-02-08T15:11:04Z
dc.date.available2026-02-08T15:11:04Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractA significant challenge lies in renewable energy sources incapacity to generate high voltages and their limited life spans when subjected to high-ripple conditions. This study introduces an innovative Sugeno-type neuro-fuzzy controller for an interleaved power converter configuration aimed at mitigating the input current ripples associated with these renewable energy sources, directly addressing the longevity concern controlled by an advanced neuro-fuzzy controller. The proposed converter employs a switched capacitor (SC) cell to amplify the generated voltage within the boost converter framework. Key attributes of the proposed converter include high voltage gain, enhanced efficiency and the utilization of short-duty ratio time intervals to minimize conduction power losses at elevated voltages. Furthermore, through interleaved configuration, the current ripple from the source is diminished while the SC cell concurrently amplifies the voltage gain. A Sugeno-type neuro-fuzzy control method, based on artificial intelligence, is employed for the proposed converter to drive the switches and produce an accurate output voltage. Since the converter is primarily built on a fuzzy controller, the proposed method is mathematically simple and easy to implement. The main contribution of the proposed control approach lies in the sampling of both the input and reference, as well as the output voltages, and the generation of precise duty cycles based on the sampled reference output voltage. Due to its capability of generating high voltages, the proposed converter and control system are suitable for use in DC grids and vehicle-to-grid applications. © The Author(s) 2025.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK
dc.identifier.doi10.1007/s00521-025-11034-7
dc.identifier.endpage8923
dc.identifier.issn0941-0643
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85219037220
dc.identifier.scopusqualityQ1
dc.identifier.startpage8895
dc.identifier.urihttps://doi.org/10.1007/s00521-025-11034-7
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5223
dc.identifier.volume37
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofNeural Computing and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzScopus_KA_20260207
dc.subjectArtificial intelligence-based controller
dc.subjectDC–DC boost converter
dc.subjectGrid integration
dc.subjectLong life span
dc.subjectLow-input current ripple
dc.subjectSugeno algorithm
dc.subjectVoltage gain
dc.titleEnhanced grid stability and prolonging life span in renewable energy power converters using an advanced Sugeno-type AI-based neuro-fuzzy control
dc.typeArticle

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