首页    期刊浏览 2022年05月21日 星期六
登录注册

文章基本信息

  • 标题:Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration
  • 本地全文:下载
  • 作者:Iman Sadeghkhani ; Abbas Ketabi ; Rene Feuillet
  • 期刊名称:Advances in Artificial Intelligence
  • 印刷版ISSN:1687-7470
  • 电子版ISSN:1687-7489
  • 出版年度:2013
  • 卷号:2013
  • DOI:10.1155/2013/316985
  • 出版社:Hindawi Publishing Corporation
  • 摘要:This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages.
国家哲学社会科学文献中心版权所有