Transformer hot spot temperature estimation with OFWF cooling using grey wolf algorithm

Articles in Press

Document Type : Original Article

Authors

1 Department of Engineering, Imam Khomeini International University, Qazvin, Iran

2 Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

Abstract

Transformers are one of the most important and expensive components of the power network. The importance of corect operation of transformer is such that sustainable electricity supply for the consumer and increasing the reliability of the power grid without transformer health is not possible. Transformer health requires careful planning for them. Transformer hot spot temperature(HST) needs to be calculated for accurate transformer planning. Today, very large transformers are made that conventional cooling methods are not efficient for them. Therefore, it is necessary to use newer and more efficient cooling methods. Oil forced - water forced (OFWF) cooling has been less considered from engineers, designers and researchers. This cooling method despite high efficiency has received less attention from researchers. The use of heuristic algorithms for transformer HST estimation, regardless of their high efficiency, accuracy and appropriate speed have been less considered by investigators, therefore in this paper, in order to increase the accuracy of the transformer HST estimation, the grey wolf optimization(GWO) algorithm is used. This algorithm requires less memory to perform optimization calculations, on the other hand has high run speed and accuracy for optimization. Moreover, in order to increase the accuracy of the transformer HST estimation, dynamic model is used. The results show that the GWO algorithm increases speed and accuracy of the transformer HST estimation. Furthermore, the results indicate that the OFWF cooling method is very efficient for large power transformers.

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Tabriz Journal of Electrical Engineering

Articles in Press, Corrected Proof
Available Online from 17 February 2025

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