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dc.contributor.authorInan R.
dc.contributor.authorBarut M.
dc.contributor.authorKarakaya F.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2014
dc.identifier.isbn9781849198158
dc.identifier.urihttps://hdl.handle.net/11480/734
dc.descriptionALTERA;Cummins Generator Technologies;Emerson;et al.;Knowledge Transfer Network KTN;OMICRONen_US
dc.description7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014 -- 8 April 2014 through 10 April 2014 -- Manchester -- 107595en_US
dc.description.abstractThis paper presents a hardware in the loop (HIL) system including the implementation of an Extended Kalman Filter (EKF) based estimator on the Field Programmable Gate Array (FPGA) for speed-sensorless control of IM. The implemented EKF algorithm simultaneously estimates stator currents (is? and isß), stator fluxes (?s? and ?sß), rotor angular velocity (?m), and load torque (tL) by assuming that stator voltages and currents are available. The HIL system also includes stator currents and fluxes based IM model which provides actual stator currents to the EKF algorithm and is also utilized to validate the flux, speed and load torque estimations of the implemented EKF algorithm. Virtex-5 VSX110T FPGA evolution board is used for this real-time application. The FPGA board is programmed via Very High Speed Integrated Circuit Hardware Description Language (VHDL) in order to develop both IM model and the EKF algorithm. ISE Design Suit Interface is used as debugger and compiler. The results obtained from the EKF and IM model developed on FPGA are graphically compared to verify the sufficiency of estimation performance of the EKF algorithm and demonstrate that EKF algorithm is implemented successfully with less computational time (less sampling time for each recursive operation) due to the inherent parallel signal processing ability of FPGA.en_US
dc.language.isoengen_US
dc.publisherInstitution of Engineering and Technologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtended kalman filteren_US
dc.subjectFPGAen_US
dc.subjectInduction motoren_US
dc.subjectSpeed sensorless controlen_US
dc.titleFPGA implementation of extended Kalman Filter for speedsensorless control of induction motorsen_US
dc.typeconferenceObjecten_US
dc.relation.journal7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014en_US
dc.departmentNiğde ÖHÜen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthor[0-Belirlenecek]


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