Pradhan, MK and Ramu, TS (2004) On-line Monitoring of Temperature in Power Transformers using Optimal Linear Combination of ANNs. In: Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, 19-22 Sep. 2004, Indianapolis, USA, pp. 70-73.
Inordinate temperature rise in a power transformer due to load current is known to be the most important factor in causing rapid degradation of its insulation and decides the optimum load catering ability or the loadability of a transformer. The Top Oil Temperature (TOT) and Hottest Spot Temperature (HST) being natural outcome of this process, an accurate estimation of these parameters is of particular importance. IEEE/IEC among others, have proposed procedure to estimate the temperatures, however, the accuracy of the predictions are not always as good as are desired. Unacceptable temperature rise may occur due to several fault conditions other than overloading, and hence warrant an online monitoring of the transformer. This paper presents an improved model for predicting TOT and HST based on Artificial Neural Network (ANN). A series of network archilecture (p-trained network) have been proposed and trained for working out this task and are further optimally combined to give an improved accuracy.
|Item Type:||Conference Paper|
|Additional Information:||©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||Hot Spot Temperature;Top Oil Temperature;ANN;Optimal Combination;Prediction Accuracy|
|Department/Centre:||Division of Electrical Sciences > High Voltage Engineering (merged with EE)|
|Date Deposited:||11 Feb 2008|
|Last Modified:||19 Sep 2010 04:39|
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