• DocumentCode
    3603565
  • Title

    Analysis of a Nonintrusive Efficiency Estimation Technique for Induction Machines Compared to the IEEE 112B and IEC 34-2-1 Standards

  • Author

    Gajjar, Chetan S. ; Kinyua, Jamlick M. ; Khan, Mohamed A. ; Barendse, Paul S.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Cape Town, Cape Town, South Africa
  • Volume
    51
  • Issue
    6
  • fYear
    2015
  • Firstpage
    4541
  • Lastpage
    4553
  • Abstract
    International efficiency testing standards, such as the IEEE 112-B and IEC 34-2-1, can determine the efficiency of an induction machine (IM) accurately at the cost of hindering the machine´s productivity. Alternatively, various methods used to determine a machine´s efficiency in situ do so at the cost of accuracy. This paper proposes a method that determines an IM´s efficiency over a range of load conditions from tests conducted and centered around one thermally stable load point in the least intrusive manner possible. The results are then compared to those of the IEEE 112-B and IEC 34-2-1 motor testing standards using segregated loss analysis. It was found that despite the proposed algorithm being unable to accurately determine core losses, the efficiency of a machine can be estimated to within 0.5%-2.1% and 1.1%-1.7% error, when compared to the IEEE 112-B and IEC 34-2-1 standards, respectively over a 25%-150% machine load profile (dependent upon the proposed method´s implementations).
  • Keywords
    IEC standards; IEEE standards; asynchronous machines; estimation theory; machine testing; IEC 34-2-1 motor testing standards; IEEE 112-B motor testing standards; IM efficiency; core losses; induction machines; international efficiency testing standards; loss analysis; machine load profile; nonintrusive efficiency estimation technique; Estimation; IEC standards; Induction motors; Rotors; Stators; Temperature measurement; Efficiency estimation; PBIL; efficiency estimation; induction motors; non-intrusive; nonintrusive; parameter estimation; population-based incremental learning (PBIL); segregated loss analysis;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2015.2453257
  • Filename
    7152919