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Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs

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dc.citedby3
dc.contributor.authorAmin I.K.en_US
dc.contributor.authorNasir Uddin M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAlam A.H.M.Z.en_US
dc.contributor.authorid10040907100en_US
dc.contributor.authorid55663372800en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57195185389en_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 current control; Electric drives; Electric fault currents; Electric power system control; Electric power transmission networks; Energy conversion; Fuzzy inference; Reactive power; Robust control; Two term control systems; Wind power; Adaptive network fuzzy inference systems; ANFIS; Conventional proportional integrals; Doubly fed induction generator (DFIG); Doubly fed induction generators; Neuro-fuzzy controller; Voltage dip; Wind energy conversion system; Electric machine controlen_US
dc.description.abstractThis paper presents an adaptive neuro-fuzzy controller (NFC)to deal with grid voltage dip conditions for grid-connected operation of doubly fed induction generator (DFIG)driven wind energy conversion system (WECS). Due to the partial scale power converters, wind turbines based on DFIG are very sensitive to grid disturbances. Current saturation at the rotor side converter (RSC)and overvoltage at the dc-link are the major concerns of DFIG driven WECS during grid-voltage fluctuation. In synchronous reference frame, an oscillatory stator flux appears during voltage dip and it is difficult to suppress with conventional proportional-integral (PI)controllers considering nonlinear system dynamics. Therefore, an adaptive-network fuzzy inference system based NFC is proposed in this paper to handle the system uncertainties and minimize the effect of grid voltage fluctuations. During normal operation, the proposed controller aims to regulate the currents as demanded by the reference real and reactive power. Under voltage dip condition, the controllers adjust the positive sequence d-q axis current components both at the grid and rotor sides by supplying required reactive power to the grid. The negative sequence reference currents at rotor end actuate the demagnetization effect of minimizing the impact of voltage dips. The simulation results exhibit the proposed NFC performance through its robust control over the rotor side currents and bus voltage during both the voltage dip and normal operation. � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8785362
dc.identifier.doi10.1109/IEMDC.2019.8785362
dc.identifier.epage2106
dc.identifier.scopus2-s2.0-85070957469
dc.identifier.spage2101
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070957469&doi=10.1109%2fIEMDC.2019.8785362&partnerID=40&md5=e4dd6042fada1a9d29e256368e965e1a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24666
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019
dc.titleAdaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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