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Prediction of top-oil temperature for transformers using neural networks

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3 Author(s)
Qing He ; Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA ; Jennie Si ; Tylavsky, D.J.

Artificial neural networks represent a growing new technology as indicated by a wide range of proposed applications. At a substation, when the transformer'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 plan. To decide on whether to deploy either of these two strategies, one should be able to predict the transformer temperature accurately. This paper explores the possibility of using artificial neural networks for predicting the top-oil temperature of transformers. Static neural networks, temporal processing networks and recurrent networks are explored for predicting the top-oil temperature of transformers. The results using different networks are compared with the auto regression linear model

Published in:

Power Delivery, IEEE Transactions on  (Volume:15 ,  Issue: 4 )