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
Link To Document :
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