On-load motor parameter identification using univariate dynamic encoding algorithm for searches / Jong Kwon Kim in IEEE transactions on energy conversion, Vol. 23 n°3 (Septembre 2008)  

Public ISBD [article]  inIEEE transactions on energy conversion > Vol. 23 n°3 (Septembre 2008) . - pp. 804 - 813 Titre : On-load motor parameter identification using univariate dynamic encoding algorithm for searches Type de document : texte imprimé Auteurs : Jong Kwon Kim, Auteur ; Taegyu Kim, Auteur ; Youngsu Park, Auteur Année de publication : 2008 Article en page(s) : pp. 804 - 813 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Encoding  induction motors  machine theory  optimisation Résumé : Parameter identification of an induction motor has long been studied either for vector control or fault diagnosis. This paper addresses parameter identification of an induction motor under on-load operation. For estimating electrical and mechanical parameters in the motor model from the on-load data, unmeasured initial states and load torque profile have to be also estimated for state evaluation. Since gradient of cost function for the auxiliary variables are hard to be derived, direct optimization methods that rely on computational capability should be employed. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS), recently developed by the authors, is applied to the identification of whole unknown variables with measured voltage, current, and velocity data. Profiles of motor parameters estimated with uDEAS are reasonable, and estimation time is 2 s on average, which is quite fast as compared with other direct optimization methods. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4560057&sortType%3Das [...] [article] On-load motor parameter identification using univariate dynamic encoding algorithm for searches [texte imprimé] / Jong Kwon Kim, Auteur ; Taegyu Kim, Auteur ; Youngsu Park, Auteur . - 2008 . - pp. 804 - 813. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°3 (Septembre 2008) . - pp. 804 - 813 Mots-clés : Encoding  induction motors  machine theory  optimisation Résumé : Parameter identification of an induction motor has long been studied either for vector control or fault diagnosis. This paper addresses parameter identification of an induction motor under on-load operation. For estimating electrical and mechanical parameters in the motor model from the on-load data, unmeasured initial states and load torque profile have to be also estimated for state evaluation. Since gradient of cost function for the auxiliary variables are hard to be derived, direct optimization methods that rely on computational capability should be employed. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS), recently developed by the authors, is applied to the identification of whole unknown variables with measured voltage, current, and velocity data. Profiles of motor parameters estimated with uDEAS are reasonable, and estimation time is 2 s on average, which is quite fast as compared with other direct optimization methods. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4560057&sortType%3Das [...]

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