Construction of power transformer thermal models using intelligent learning

被引：0

作者：

Tang, W.H. ^{[1
]}

Nuttall, K.I. ^{[1
]}

Wu, Q.H. ^{[1
]}

Richardson, Z. ^{[1
]}

Simonson, E. ^{[1
]}

机构：

[1] Dept. of Elec. Eng. and Electronics, The University of Liverpool, Liverpool, L69 3GJ, United Kingdom

来源：

Proceedings of the Universities Power Engineering Conference | 2000年

关键词：

Condition monitoring - Genetic algorithms - Learning systems - Neural networks - Temperature measurement;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A new transient-state thermal model for power transformer for temperature prediction was constructed, based on the conventional thermal model. Two different intelligent learning methods, genetic algorithm (GA) and artificial neural network (ANN), were employed to construct thermal models from the on-site measurements. A comparison with experimental measurements showed that the transient-state model with recursion of the outputs, obtained using the GA learning, has a better performance than that of the conventional model.

引用

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