IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society 2010
DOI: 10.1109/iecon.2010.5674931
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Identification of induction motor at standstill using artificial neural network

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Cited by 7 publications
(11 citation statements)
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“…If the compensation of VSI nonlinearity is not taken into account, it is evident from (19) that the estimated R=R a +∆R will be significantly larger than R a , wh ich is experimentally shown in Fig. 9(b).…”
Section: (C)mentioning
confidence: 92%
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“…If the compensation of VSI nonlinearity is not taken into account, it is evident from (19) that the estimated R=R a +∆R will be significantly larger than R a , wh ich is experimentally shown in Fig. 9(b).…”
Section: (C)mentioning
confidence: 92%
“…Often on-line estimation of parameters is required so the controller parameters and system conditions can be updated, especially for sensorless control [1]- [5] and optimal PI controller design. Much literature has made contributions [4]- [19] to obtain parameters with d ifferent on-line estimat ion strategies fro m the measured machine electrical signals, such as the model reference adaptive system (M RAS) [4]- [7], recursive least square algorithms (RLS) [8]- [11], Neu ral Kan Liu, Z.Q. Zhu, Fellow, IEEE, Qiao Zhang, Member, IEEE, and Jing Zhang P Network (NN) technology [12], [15], [19], and extended Kalman filter (EKF) [13].…”
Section: Introductionmentioning
confidence: 99%
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