Induction motors (IM) has different rotor types: single cage, double cage or deep bar cage. In major applications, any induction motor is represented by a single cage model which is not suitable to characterize the dynamic behavior of all IMs. In fact, IM robust control depends mainly on the IM model and on the accuracy of the parameters identification. This paper presents the modeling of the single cage, double cage and deep bar induction motor by using a Model of Invariant Parameters. An offline IM identification based upon steady state electric quantities (voltage, stator current and active power) is developed by performing a locked rotor test for different frequencies. The linear Least Squares Technique (LST) and the Genetic Algorithm (GA) are used. The sensitivity to measurement errors is evaluated for each method. Thus the GA identification is used to classify the motor according to its rotor type. The approach is simulated using Matlab 7.1.0, and applied to twenty IM with known manufacturer parameters and rotor types. These parameters are used to establish a simulation database so as to validate the classification and identification procedure. GA is judged more efficient since it persists and converges for a measurement noise of 5%.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Linear least squares and genetic algorithm based induction motor identification and classification by performing a locked rotor test at variable frequency


    Beteiligte:
    Gassara, Nader (Autor:in) / Bahloui, Wissem (Autor:in) / Chaabene, Maher (Autor:in) / Kamoun, M.B.A. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2011


    Format / Umfang :

    12 Seiten, 2 Bilder, 2 Tabellen, 20 Quellen



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch





    Motor locked-rotor protection system, motor locked-rotor protection method and motor

    YANG KEWEN / XU SHIHUA / WENG SHUN et al. | Europäisches Patentamt | 2020

    Freier Zugriff


    Identification of Linear Time-Varying Systems Using a Modified Least Squares Algorithm

    Lozano, R. / Dimogianopoulos, D. / Mahony, R. et al. | British Library Conference Proceedings | 2000


    Multi-variable series infinite impulse response least squares algorithm

    Juraj Havelka, / Ante Marusic, / Igor Kuzle, | IEEE | 2007