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A Novel Graphical Processing Unit Method for Power Systems Security Analysis.

机译:电力系统安全分析的新型图形处理单元方法。

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摘要

There is an increasing need for computational power to drive software tools used in power systems planning and operations, since the emergence of modern energy markets and recent renewable generation technology fundamentally alters how energy flows through the existing power grid. While special-purpose hardware, including supercomputers, has been explored for this purpose, inexpensive commodity hardware is another way of getting increased computational power within the power systems control centers.;Adding General-Purpose Graphical Processing Units (GPGPUs) to the nodes in a control center's existing computational platform is a significantly lower expense than adding an equivalent number of new nodes and the infrastructure to support them. If accelerating computations with GPGPUs can halve the time needed for for a set of contingencies to run on a set of given computational nodes, freeing up crucial minutes for analysis of additional contingencies, the investment can be worth the costs. Yet this would be considered a quite modest speedup for GPGPU computing if the problem is conditioned in a way that maps well to the architecture and programming model of the GPGPU.;The novel method for GPGPU contingency analysis and its variants presented in this thesis allows that process of speedup to be taken substantially further, since it re-maps as much of the computation as possible to be a series of dense vector operations based on simple arithmetic that is conservative with respect to data movement and flexible with respect to implementation details such as thread block size. Where sparse matrix operations cannot be avoided, this method, by slicing across contingencies, re-maps such operations to the much more efficient problem of a sparse matrix multiplied by a block of dense vectors larger than the matrix itself. The method applies to (N-1-1), (N-2), and (N-3) contingencies with little modification and little increase in computational burden or data movement per contingency. The method is designed to accommodate systems of thousands to tens of thousands of buses, if need be, with the large power systems resulting from control area consolidation in mind.
机译:由于现代能源市场的出现和最新的可再生能源发电技术从根本上改变了能源流过现有电网的方式,因此越来越需要计算能力来驱动电力系统规划和运营中使用的软件工具。尽管为此目的已探究了包括超级计算机在内的专用硬件,但廉价的商用硬件是在电力系统控制中心内获得更高计算能力的另一种方法。将通用图形处理单元(GPGPU)添加到服务器的节点中与添加同等数量的新节点和支持它们的基础结构相比,控制中心现有的计算平台的成本大大降低。如果使用GPGPU加速计算可以将一组偶发事件在一组给定的计算节点上运行所需的时间减半,从而腾出关键时间来分析其他偶发事件,那么这笔投资是值得的。但是,如果以与GPGPU的体系结构和编程模型很好地对应的方式解决问题,则这对于GPGPU计算来说是相当适度的提速。本文提出的新颖的GPGPU权变分析方法及其变体允许:加速过程要进一步进行,因为它会根据简单的算术将尽可能多的计算重新映射为一系列密集的矢量运算,这种简单算术对数据的移动是保守的,而对实现细节则是灵活的,例如线程块大小。在无法避免稀疏矩阵运算的情况下,此方法通过对意外事件进行切片,将此类运算重新映射到效率更高的稀疏矩阵乘以比矩阵本身更大的密集矢量块的问题。该方法适用于(N-1-1),(N-2)和(N-3)偶发事件,几乎没有修改,每偶发事件的计算负担或数据移动几乎没有增加。该方法旨在容纳成千上万的总线系统(如果需要),同时考虑到控制区域合并产生的大型电源系统。

著录项

  • 作者

    Miller, Laurie Elizabeth.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Engineering Electronics and Electrical.;Computer Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 261 p.
  • 总页数 261
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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