Advanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified Golden Sine Algorithm

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dc.authoridDANDIL, Besir/0000-0002-3625-5027
dc.authoridKAYISLI, KORHAN/0000-0001-8456-1478
dc.authoridTANYILDIZI, ERKAN/0000-0003-2973-9389
dc.authoridCOTELI, Resul/0000-0002-7365-4318
dc.authoridACIKGOZ, Hakan/0000-0002-6432-7243
dc.contributor.authorAcikgoz, Hakan
dc.contributor.authorCoteli, Resul
dc.contributor.authorTanyildizi, Erkan
dc.contributor.authorDandil, Besir
dc.contributor.authorKayisli, Korhan
dc.date.accessioned2024-09-18T20:55:34Z
dc.date.available2024-09-18T20:55:34Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThree-phase Pulse-Width Modulated (PWM) rectifiers used between the power grid and the load in applications requiring DC voltage have features such as high efficiency, high power factor, and low harmonics. This paper proposes a hybrid control approach to improve the dynamic performance of three-phase PWM rectifiers under different operating conditions. Operating conditions are considered as step response, internal disturbance, and regenerative operation. First, Interval Type-2 Fuzzy Neural Network (IT2FNN) is designed and then antecedent and consequent parameters of IT2FNN are optimized with Modified Golden Sine Algorithm (GoldSA-II). The dynamic performance of the hybrid controller, named GoldSA-II-IT2FNN, is analyzed for all operating conditions in Matlab/Simulink environment. The simulation studies are realized to evaluate the performance of the proposed controller. In the simulations, settling times of proposed controller are observed as 27.2 ms, and 10.8 ms for step response, respectively. Moreover, recovery times are calculated as being 12 ms to 5.5 ms for internal disturbance, and 7.2 ms to 19 ms for regenerative operation, respectively. The obtained results demonstrate that the proposed controller not only provides better dynamic performance but also improves the stability of PWM rectifier.en_US
dc.identifier.doi10.1080/15325008.2023.2185838
dc.identifier.issn1532-5008
dc.identifier.issn1532-5016
dc.identifier.scopus2-s2.0-85150502612en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1080/15325008.2023.2185838
dc.identifier.urihttps://hdl.handle.net/20.500.12483/11926
dc.identifier.wosWOS:000953302000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofElectric Power Components and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPWM rectifieren_US
dc.subjectinterval type-2 fuzzy neural networken_US
dc.subjectGolden Sine Algorithmen_US
dc.subjectvoltage-oriented controlen_US
dc.subjectpower qualityen_US
dc.titleAdvanced Control of Three-Phase PWM Rectifier Using Interval Type-2 Fuzzy Neural Network Optimized by Modified Golden Sine Algorithmen_US
dc.typeArticleen_US

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