Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55691
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dc.contributor.authorJirasak Laowanitwattanaen_US
dc.contributor.authorSermsak Uatrongjiten_US
dc.date.accessioned2018-09-05T02:59:52Z-
dc.date.available2018-09-05T02:59:52Z-
dc.date.issued2016-01-18en_US
dc.identifier.other2-s2.0-84966470575en_US
dc.identifier.other10.1109/ICEMS.2015.7385302en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966470575&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55691-
dc.description.abstract© 2015 IEEE. Applying the Extended Kalman Filter (EKF) technique to estimate states and parameters of an induction motor usually requires information about the stator currents, voltages, and sometimes the rotor rotating speed is also utilized. In many usage conditions, the stator voltages have constant magnitude and frequency. According to this observation, in this work, the EKF based technique for estimating both states and parameters of induction motor's dynamic model which employs just stator currents is presented. To verify the proposed technique, three phase current data obtained from simulating a 380 V 50 Hz 4 poles squirrel cage induction motor are applied to this algorithm. The numerical experiment results indicate that the method can estimate the induction motor's states and parameters with satisfactory accuracy.en_US
dc.subjectEnergyen_US
dc.subjectEngineeringen_US
dc.titleInduction motor states and parameters estimation using Extended Kalman Filter with reduced number of measurementsen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2015 18th International Conference on Electrical Machines and Systems, ICEMS 2015en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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