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  • 标题:Genetic Algorithm Based Reactive Power Management by SVC
  • 其他标题:Genetic Algorithm Based Reactive Power Management by SVC
  • 本地全文:下载
  • 作者:Md. Imran Azim ; Md. Fayzur Rahman
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2014
  • 卷号:4
  • 期号:2
  • 页码:200-206
  • 语种:English
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要:This paper contains an approach of a generalized optimization formulation regarded as genetic algorithm with a view to determining the optimal location of distributed generators in 10-bus network offering reactive power capability. It is certainly the case that the reactive power management plays a noteworthy role, when it is required to improve not just the voltage profile but the voltage stability as well. In this paper, the requisite reactive power planning has been precisely solved by the evolutionary genetic algorithm, which is based on biological metaphor, in which best individuals are selected among parents and offspring generation. In addition, genetic algorithm does not need initial information about the system to begin the searching process since it works only with the chromosomes which will be optimized according to the objective functions and the proper constraints. As far as this paper goes, the injection of 228.5469553MVAR reactive power by Static Var Compensator (SVC) is enough to maintain voltage stability throughout the system.DOI:http://dx.doi.org/10.11591/ijece.v4i2.5879
  • 其他摘要:This paper contains an approach of a generalized optimization formulation regarded as genetic algorithm with a view to determining the optimal location of distributed generators in 10-bus network offering reactive power capability. It is certainly the case that the reactive power management plays a noteworthy role, when it is required to improve not just the voltage profile but the voltage stability as well. In this paper, the requisite reactive power planning has been precisely solved by the evolutionary genetic algorithm, which is based on biological metaphor, in which best individuals are selected among parents and offspring generation. In addition, genetic algorithm does not need initial information about the system to begin the searching process since it works only with the chromosomes which will be optimized according to the objective functions and the proper constraints. As far as this paper goes, the injection of 228.5469553MVAR reactive power by Static Var Compensator (SVC) is enough to maintain voltage stability throughout the system. DOI: http://dx.doi.org/10.11591/ijece.v4i2.5879
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