When a transformer's windings get too hot, either load has to be reduced (in the short term) or another transformer bay needs to be installed (in the long run). To be able to predict when either of these remedial schemes must be used, we need to be able to predict the transformer's temperature accurately. Our experimentation with various discretization, schemes and models, convinced us that the linear and nonlinear semiphysical models we were using to predict transformer temperature were near optimal and that other sources of input-data error were frustrating our attempts to reduce the prediction error further. In this paper we explore some of the sources of error that affect top-oil temperature prediction. We show that the traditional top-oil rise model has incorrect dynamic behavior and show that another model proposed corrects this problem. We show that the input error caused by database quantization, remote ambient temperature monitoring and low sampling rate account for about 2/3 of the error experienced with field data. It is the opinion of the authors that most of this difference is due to the absence of significant driving variables, rather than the approximation used in constructing a linear semiphysical model.
展开▼
