Adaptive neuro-fuzzy inference system combined with genetic algorithm to improve power extraction capability in fuel cell applications

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dc.authoridInci, Mustafa/0000-0002-0900-5946
dc.contributor.authorSavrun, Murat Mustafa
dc.contributor.authorInci, Mustafa
dc.date.accessioned2025-01-06T17:38:05Z
dc.date.available2025-01-06T17:38:05Z
dc.date.issued2021
dc.description.abstractThis study introduces an improved ANFIS based MPPT method to maximize the power extraction capability of the FC-connected system. The proposed method is tested in a stand-alone system that consists of an FC in the power rating of 1.9 kW, a boost dc-dc converter, local consumer load, and processor unit. The energy transfer between FC and load is handled through the adjustment of a duty cycle of the dc-dc converter. In this context, the output voltage of FC is controlled by the duty cycle to track the MPP. The proposed method called GA-ANFIS computes optimum reference voltages to control the FC output voltage optimally. The GA-ANFIS uses a reduced-size training dataset extracted by GA to train the ANFIS in comparison with conventional ANFIS. Unlike the existing methods, the proposed method tracks the MPP by merely monitoring FC voltage during operation. Besides, it performs precise MPP tracking by considering pressure & temperature variations. Thus, the proposed method provides reduced computational load owing to its current features. The performance of the proposed method compared with the traditional methods like ANFIS and PI. The power extraction ratings and efficiency values validate the viability and effectiveness of the proposed method (>98%). (c) 2021 Elsevier Ltd. All reserved.
dc.identifier.doi10.1016/j.jclepro.2021.126944
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.scopus2-s2.0-85104093338
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jclepro.2021.126944
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2470
dc.identifier.volume299
dc.identifier.wosWOS:000647718800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofJournal of Cleaner Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGA-ANFIS
dc.subjectFuel cell
dc.subjectMaximum power extraction
dc.subjectOptimization
dc.subjectOperational changes
dc.titleAdaptive neuro-fuzzy inference system combined with genetic algorithm to improve power extraction capability in fuel cell applications
dc.typeArticle

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