Effect of quantization and sampling time on transformers thermal performance and parameters calculation

Daniel Tylavsky; Q. He; Jennie Si; G. A. McCulla; J. R. Hunt

Effect of quantization and sampling time on transformers thermal performance and parameters calculation

Daniel Tylavsky, Q. He, Jennie Si, G. A. McCulla, J. R. Hunt

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HST's and TOT's) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and non-physical models we were using to predict transformer TOT were correctly modeling the TOT behavior. Our experience convinced us that noisy input data and the absence of data on significant driving variables, not model deficiencies, were frustrating our attempts to reduce the prediction error further. In this paper, we discuss the body of research that leads us to these conclusions.

Original language	English (US)
Title of host publication	Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Publisher	IEEE
Pages	1345-1351
Number of pages	7
Volume	2
State	Published - 1999
Event	Proceedings of the 1999 IEEE Industry Applications Conference - 34th IAS Annual Meeting - Phoenix, AZ, USA Duration: Oct 3 1999 → Oct 7 1999

Other

Other	Proceedings of the 1999 IEEE Industry Applications Conference - 34th IAS Annual Meeting
City	Phoenix, AZ, USA
Period	10/3/99 → 10/7/99

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

Tylavsky, D, He, Q, Si, J, McCulla, GA & Hunt, JR 1999, Effect of quantization and sampling time on transformers thermal performance and parameters calculation. in Conference Record - IAS Annual Meeting (IEEE Industry Applications Society). vol. 2, IEEE, pp. 1345-1351, Proceedings of the 1999 IEEE Industry Applications Conference - 34th IAS Annual Meeting, Phoenix, AZ, USA, 10/3/99.

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abstract = "Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HST's and TOT's) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and non-physical models we were using to predict transformer TOT were correctly modeling the TOT behavior. Our experience convinced us that noisy input data and the absence of data on significant driving variables, not model deficiencies, were frustrating our attempts to reduce the prediction error further. In this paper, we discuss the body of research that leads us to these conclusions.",

author = "Daniel Tylavsky and Q. He and Jennie Si and McCulla, {G. A.} and Hunt, {J. R.}",

year = "1999",

language = "English (US)",

volume = "2",

pages = "1345--1351",

booktitle = "Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)",

publisher = "IEEE",

note = "Proceedings of the 1999 IEEE Industry Applications Conference - 34th IAS Annual Meeting ; Conference date: 03-10-1999 Through 07-10-1999",

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AU - Tylavsky, Daniel

AU - He, Q.

AU - Si, Jennie

AU - McCulla, G. A.

AU - Hunt, J. R.

PY - 1999

Y1 - 1999

N2 - Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HST's and TOT's) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and non-physical models we were using to predict transformer TOT were correctly modeling the TOT behavior. Our experience convinced us that noisy input data and the absence of data on significant driving variables, not model deficiencies, were frustrating our attempts to reduce the prediction error further. In this paper, we discuss the body of research that leads us to these conclusions.

AB - Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HST's and TOT's) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and non-physical models we were using to predict transformer TOT were correctly modeling the TOT behavior. Our experience convinced us that noisy input data and the absence of data on significant driving variables, not model deficiencies, were frustrating our attempts to reduce the prediction error further. In this paper, we discuss the body of research that leads us to these conclusions.

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PB - IEEE

T2 - Proceedings of the 1999 IEEE Industry Applications Conference - 34th IAS Annual Meeting

Y2 - 3 October 1999 through 7 October 1999

ER -

Effect of quantization and sampling time on transformers thermal performance and parameters calculation

Abstract

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ASJC Scopus subject areas

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