• DocumentCode
    162869
  • Title

    Methods to detect incorrect fan status for transformer thermal models

  • Author

    Rao, Smitha ; Tylavsky, Daniel ; Alteneder, Ken ; Brown, Kenneth E. ; Gunawardena, Jason ; LaRose, Thomas

  • Author_Institution
    Sch. of Electr. Comput. & Energy Eng., Arizona State Univ., Tempe, AZ, USA
  • fYear
    2014
  • fDate
    7-9 Sept. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Transformers are seldom loaded to their maximum capacity as per the existing industry practices. The ultimate goal of this research project is to develop a method for predicting the maximum dynamic loading capability without violating the thermal limits of the transformer´s insulation. Dynamic loading must account for, at minimum, load magnitude and shape, the ambient temperature, the external cooling conditions and the thermal limits. This paper discusses methods of detecting irregularities in the cooling mode transitions for substation distribution transformers. The two HST and TOT models considered in this paper are the non-linear IEEE model and the model built using linear regression techniques.
  • Keywords
    cooling; fans; regression analysis; substations; transformer insulation; HST models; TOT models; cooling mode transitions; external cooling conditions; incorrect fan status detection; linear regression techniques; maximum dynamic loading capability; nonlinear IEEE model; substation distribution transformers; transformer insulation; transformer thermal models; Cooling; Data models; Load modeling; Mathematical model; Oil insulation; Reliability; Temperature measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    North American Power Symposium (NAPS), 2014
  • Conference_Location
    Pullman, WA
  • Type

    conf

  • DOI
    10.1109/NAPS.2014.6965406
  • Filename
    6965406