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Optimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converter

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dc.citedby0
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorAnsari S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorUstun T.S.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57218906707en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid43761679200en_US
dc.date.accessioned2025-03-03T07:46:05Z
dc.date.available2025-03-03T07:46:05Z
dc.date.issued2024
dc.description.abstractThis paper presents a study on Maximum Power Point Tracking (MPPT) employing a DC-DC boost converter in MATLAB. The approach utilizes a genetic algorithm to determine optimal values for the membership functions (MFs) and the configuration of rules in a Mamdani fuzzy inference system. The fuzzy system is designed to manage the error signal derived from comparing the reference voltage of the Perturb and Observe (P&O) controller with the photovoltaic (PV) voltage. Inputs to the fuzzy controller include the error and its rate of change, with the output controlling the Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) pulses. The optimization aims to minimize the Root Mean Square Error (RMSE) to identify the most effective MFs and rules for the fuzzy controller, ultimately enhancing the MPPT output of the boost converter. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICCIGST60741.2024.10717559
dc.identifier.scopus2-s2.0-85208431862
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85208431862&doi=10.1109%2fICCIGST60741.2024.10717559&partnerID=40&md5=507a1fada3e2168338695712dbb8b135
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36954
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024 - Proceedings
dc.subjectAdaptive boosting
dc.subjectBoost converter
dc.subjectFuzzy control
dc.subjectFuzzy inference
dc.subjectFuzzy rules
dc.subjectMOSFET devices
dc.subjectRoot loci
dc.subjectSurface discharges
dc.subjectBOOST converter
dc.subjectDc - dc boost converters
dc.subjectDC-DC boost genetic algorithm
dc.subjectFuzzy controllers
dc.subjectFuzzy inference systems
dc.subjectMamdani fuzzy inferences
dc.subjectMaximum Power Point Tracking
dc.subjectMemberships function
dc.subjectOptimal fuzzy logic controllers
dc.subjectOptimal values
dc.subjectMOS devices
dc.titleOptimal Fuzzy logic controller based Genetic Algorithm for control maximum power point tracking for Boost Converteren_US
dc.typeConference paperen_US
dspace.entity.typePublication
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