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  • 标题:Contingency Analysis Post-Processing With Advanced Computing and Visualization
  • 本地全文:下载
  • 作者:Yousu Chen ; Kurt Glaesemann ; Erin Fitzhenry
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2017
  • 卷号:50
  • 期号:1
  • 页码:55-60
  • DOI:10.1016/j.ifacol.2017.08.010
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractContingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation introduced by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a Western Electricity Coordinating Council planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.
  • 关键词:KeywordsContingency analysishigh performance computingparallel posting processingpower system operationvisualization
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