• DocumentCode
    1597078
  • Title

    A robust, on-line turn-fault detection technique for induction machines based on monitoring the sequence component impedance matrix

  • Author

    Lee, Sang-Bin ; Tallam, Rangarajan M. ; Habetler, Thomas G.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • Volume
    4
  • fYear
    2001
  • fDate
    6/23/1905 12:00:00 AM
  • Firstpage
    2217
  • Abstract
    A simple and robust sensorless technique for online stator winding turn fault detection based on monitoring an off-diagonal term of the sequence component impedance matrix is proposed in this paper. Due to the destructive and rapidly propagating nature of insulation failure, it is critical to detect turn faults in an early stage to minimize the financial loss. The motor nonidealities such as the variation in the supply voltage unbalance, slip-dependent influence of inherent motor asymmetry, and measurement errors must be taken into account in order to reliably detect a turn fault in its incipient stage. Simulation and experimental results on a 5 hp induction machine are given to confirm the validity of the proposed method. It is shown that the proposed turn fault detection scheme is simple and is capable of providing reliable fault detection that is immune to the motor nonidealities
  • Keywords
    computerised monitoring; fault diagnosis; induction motors; machine testing; machine theory; matrix algebra; power engineering computing; stators; 5 hp; incipient fault diagnosis; induction machine; motor nonidealities; online stator winding turn fault detection; robust sensorless technique; sequence component impedance matrix monitoring; supply voltage unbalance variation; Condition monitoring; Fault detection; Impedance; Induction machines; Insulation; Measurement errors; Propagation losses; Robustness; Stator windings; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics Specialists Conference, 2001. PESC. 2001 IEEE 32nd Annual
  • Conference_Location
    Vancouver, BC
  • ISSN
    0275-9306
  • Print_ISBN
    0-7803-7067-8
  • Type

    conf

  • DOI
    10.1109/PESC.2001.954449
  • Filename
    954449