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  • 标题:Radial Basis Function Neural Network Application to Power System Restoration Studies
  • 本地全文:下载
  • 作者:Iman Sadeghkhani ; Abbas Ketabi ; Rene Feuillet
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2012
  • 卷号:2012
  • DOI:10.1155/2012/654895
  • 出版社:Hindawi Publishing Corporation
  • 摘要:One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux 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 and duration of switching overvoltages with good accuracy.
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