In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines, mainly due to their ability to operate smoothly under fault conditions. Due to multiple stator phase arrangements, the existing fault diagnostic (FD) methods are not sufficiently universal and cannot be applied to the multiple configurations of multiphase machines. In this regard, the current research proposes a universal and effective online diagnostics technique based on the computation and monitoring of a relevant severity factor, which is defined as the ratio of the zero, negative, and positive voltage symmetrical components. The short time least square Prony’s (STLSP) approach is used to implement this method online in LabVIEW environment. In addition to the wide applicability of the method, the obviation of any motor parameter estimation and limited requirements regarding variables measurements makes the proposed approach extremely advantageous for motors with high number of phases. This article addresses at stator faults in symmetrical six-phase induction motor (6PIM). The proposed method’s experimental results on six-phase motor drive platforms demonstrate the excellent generalization capability of the proposed method, as well as high accuracy and robustness, along with the ability to accurately distinguish between the occurrence of interturn short-circuit (ITSC) faults and the presence of unbalanced supply voltage (USV) conditions. A 6PIM is put through its paces in both healthy and faulty conditions. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the occurrence of ITSC faults and USV operating conditions with high reliability, fastness, and high precision.


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    Titel :

    Online Diagnosis and Discrimination of Stator Faults in Six-Phase Induction Motors Based on Voltage Symmetrical Components


    Beteiligte:


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    2757283 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch