{"title":"The effect of quantization and sampling time on transformers thermal performance and parameters calculation","authors":"D. Tylavsky, Q. He, J. Si, G. McCulla, J. Hunt","doi":"10.1109/IAS.1999.801676","DOIUrl":null,"url":null,"abstract":"Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HSTs and TOTs) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and nonphysical 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.","PeriodicalId":125787,"journal":{"name":"Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAS.1999.801676","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Improving the utilization of transformers requires that the hot-spot and top-oil temperatures (HSTs and TOTs) be predicted accurately. Our experimentation with various discretization schemes and models, proved that many of the linear and nonlinear semi-physical and nonphysical 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.