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ANFIS based neuro-fuzzy control of dfig for wind power generation in standalone mode

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dc.citedby8
dc.contributor.authorAmin I.K.en_US
dc.contributor.authorNasir Uddin M.en_US
dc.contributor.authorMarsadek M.en_US
dc.contributor.authorid10040907100en_US
dc.contributor.authorid55663372800en_US
dc.contributor.authorid26423183000en_US
dc.date.accessioned2023-05-29T07:25:38Z
dc.date.available2023-05-29T07:25:38Z
dc.date.issued2019
dc.descriptionAdaptive control systems; Asynchronous generators; Controllers; Electric drives; Electric fault currents; Electric power generation; Fuzzy control; Fuzzy inference; Fuzzy logic; Fuzzy neural networks; Inference engines; Robust control; Two term control systems; Wind; Wind power; Adaptive network based fuzzy inference system; Doubly fed induction generator (DFIG); Doubly fed induction generators; Neural network algorithm; Neuro-fuzzy controller; Proportional-integral control; Stand-alone modes; Wind energy conversion system; Electric machine controlen_US
dc.description.abstractThis paper presents an adaptive neuro-fuzzy controller (NFC)for doubly fed induction generator (DFIG)based wind energy conversion system (WECS)to operate under standalone mode. The NFC is developed based on adaptive-network-based fuzzy inference system (ANFIS)architecture since it has the unique advantage of fast convergence combining the robustness of fuzzy logic and flexibility of neural network algorithm. For the isolated operation of DFIG-WECS, ANFIS is designed for load side converter (LSC)control. The proposed scheme demonstrates improved dynamic performance under variable wind speed and load conditions by maintaining stable output voltage. The supply frequency to the load remains stable by virtue of precise control of LSC while turbine rotation varies with fluctuating wind speed. The flux alignment is ensured by the proportional-integral (PI)control of rotor side converter. The simulation results exhibit the controller's outstanding performance through its robust control over load-voltage and supply frequency under the variation of demand load power and wind speed. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8785334
dc.identifier.doi10.1109/IEMDC.2019.8785334
dc.identifier.epage2082
dc.identifier.scopus2-s2.0-85070983251
dc.identifier.spage2077
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070983251&doi=10.1109%2fIEMDC.2019.8785334&partnerID=40&md5=471068fde7cc84b707346d7619543984
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24665
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019
dc.titleANFIS based neuro-fuzzy control of dfig for wind power generation in standalone modeen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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