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  • 标题:Delta-Bar-Delta and Directed Random Search Algorithms Application to Reduce Transformer Switching Overvoltages
  • 本地全文:下载
  • 作者:Iman Sadeghkhani ; Abbas Ketabi ; Rene Feuillet
  • 期刊名称:International Journal on Electrical Engineering and Informatics
  • 印刷版ISSN:2085-6830
  • 出版年度:2013
  • 卷号:5
  • 期号:1
  • 出版社:School of Electrical Engineering and Informatics
  • 摘要:This paper proposed an artificial neural network (ANN)-based approach to mitigate harmonic overvoltages during transformer energization. Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching overvoltages. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that it’s minimum value is corresponding to the best case switching time. Also, in this paper three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train ANNs to estimate the optimum switching instants for real time applications. ANNs training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.
  • 关键词:Artificial neural networks; delta-bar-delta; directed random search;algorithm; harmonic index; switching overvoltages; transformer energization.
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