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  • 标题:Radial Basis Function Neural Network based Approach to Estimate Transformer Harmonic Overvoltages
  • 本地全文:下载
  • 作者:Iman Sadeghkhani ; Ali Yazdekhasti ; Arezoo Mortazavian
  • 期刊名称:Advances in Computer Science and its Applications
  • 印刷版ISSN:2166-2924
  • 出版年度:2012
  • 卷号:1
  • 期号:1
  • 页码:38-44
  • 语种:English
  • 出版社:World Science Publisher
  • 摘要:This paper present an approach to evaluate overvoltages caused by transformer switching based on Radial Basis Function Neural Network (RBFNN). Such an overvoltage might damage some equipment and delay ‎power system restoration. The ‎developed ANN is trained with the worst case of the switching condition, and ‎tested for typical cases. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values of switching overvoltages with good accuracy.
  • 关键词:Artificial neural networks;harmonic overvoltages;inrush current;power system restoration;radial basis function;transformer energization.‎
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