Prediction of top-oil temperature for transformers using neural networks

Artificial neural networks represent a growing new technology as indicated by a wide range of proposed applications. At a substation, when the transfo rmer's windings get too hot, either load has to be reduced as a short-term solution, or another transformer bay has to be installed as a long-term pla n. To decide on whether to deploy either of these two strategies, one shoul d be able to predict the transformer temperature accurately, This paper exp lores the possibility of using artificial neural networks for predicting to p-oil temperature of transformers. Static neural networks, temporal process ing networks and recurrent networks are explored for predicting the top-oil temperature of transformers. The results using different networks will be compared with the auto regression linear model.