Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems

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dc.authorid0000-0002-0975-0241
dc.authorid0000-0003-2234-3453
dc.authorid0000-0002-0362-4017
dc.contributor.authorOzden, Mustafa
dc.contributor.authorErtekin, Davut
dc.contributor.authorSiano, Pierluigi
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractA crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of these energy sources and ensuring their compatibility with high-voltage AC and DC grids. This study introduces a high-gain DC-DC step-up converter that incorporates a continuous input current cell and a switched capacitor voltage-boosting output cell to address these requirements. The control process of this proposed converter is executed using an artificial neural network based on the Levenberg-Marquardt learning algorithm. The primary difference in this research lies in obtaining the artificial neural network-based controller directly from the circuit's characteristic equations, rather than generating it through another controller. A feedback control strategy has been formulated, where the artificial neural network consistently produces duty increment values based on the reference voltage. Additionally, the network's input includes not only the reference signal but also the circuit input voltage and output current value. As a result, the stability of the circuit's output voltage is maintained against variations in input voltage and load changes. A laboratory-designed workbench underwent testing, and the experimental results substantiated the theoretical inquiries and simulation outcomes.
dc.identifier.doi10.1109/ACCESS.2024.3524378
dc.identifier.endpage3631
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85214139227
dc.identifier.scopusqualityQ1
dc.identifier.startpage3613
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3524378
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5893
dc.identifier.volume13
dc.identifier.wosWOS:001394723100017
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectFuel cell
dc.subjectsmart control
dc.subjectartificial neural network
dc.subjectartificial neural network
dc.subjectgrid-connected power converter
dc.subjectgrid-connected power converter
dc.subjectlow- input current cell
dc.subjectlow- input current cell
dc.subjecthigh-voltage gain cell
dc.subjecthigh-voltage gain cell
dc.titleLevenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems
dc.typeArticle

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