Neuro-fuzzy-SVPWM switched-inductor-capacitor-based boost inverter for grid-tied fuel cell power generators, design and implementation

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dc.authorid0000-0003-2234-3453
dc.contributor.authorErtekin, Davut
dc.contributor.authorOzden, Mustafa
dc.contributor.authorDeniz, Adnan
dc.contributor.authorToprak, Muhammed Zeyd
dc.date.accessioned2026-02-08T15:15:25Z
dc.date.available2026-02-08T15:15:25Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractHydrogen energy shows promise as a renewable energy source for various applications like battery and electric vehicle charging stations, as well as grid connections. However, high current ripple from fuel cells (FCs) and inadequate voltages for grid use pose challenges. This study presents a novel solution using neural fuzzy network control in a high-gain DC-DC boost converter to address these issues. The suggested converter charges in parallel and discharges in series, minimizing the current ripple range in the fuel cell network. Additionally, the switchcapacitor cell efficiently increases the output voltage. In this study, a Neuro-fuzzy system with 9 rules is trained meticulously over 50 epochs using hybrid optimization and grid partition methods, achieving a low training error of 0.045 with 522,064 samples. The neural fuzzy network, employing the weighted average method for Defuzzification, produces duty cycle values from 0.02 to 0.5 in response to input signals. Additionally, an innovative Space Vector Pulse Width Modulation (SVPWM) approach within the inverter circuit enhances voltage generation precision and power quality for grid delivery, notably reducing current ripple and ensuring stable power supply. This combined with the neural fuzzy network in the converter efficiently converts hydrogen energy into AC voltage for seamless grid integration, revolutionizing boost converter efficiency and advancing hydrogen energy utilization across various energy sectors.
dc.description.sponsorshipthe proposed converter topology, including mathematical analysis, simulation, drafting, and paper writing. Mustafa Odzden d zden was responsible for designing and implementing the neuro-fuzzy system in both theory and laboratory, as well as writing sections related to the control process. Adnan Deniz, supervised by Davut Ertekin, presented the analysis of the inverter and SVPWM technique. Muhammed Zeyd Toprak conducted the laboratory workbench setup, tests, and measurements.
dc.identifier.doi10.1016/j.renene.2024.120469
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-85190559539
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.renene.2024.120469
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5773
dc.identifier.volume227
dc.identifier.wosWOS:001300610400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofRenewable Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectFuel cell
dc.subjectNeuro-fuzzy controller
dc.subjectDC-DC boost converter
dc.subjectSVPWM
dc.subjectInverter
dc.subjectCurrent ripple
dc.subjectReal-time DSP controller
dc.titleNeuro-fuzzy-SVPWM switched-inductor-capacitor-based boost inverter for grid-tied fuel cell power generators, design and implementation
dc.typeArticle

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