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A distributed computing approach for real-time transient stability analysis

机译:实时暂态稳定分析的分布式计算方法

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摘要

Power system online dynamic security assessment (DSA) is a challenging computing problem. A key problem in DSA is the analysis of a large number of dynamic stability contingencies every 10-20 minutes using online data. In order to speed up the transient stability analysis, parallel processing has been applied and several results can be found in the literature. In this paper, the authors present a distributed approach for real-time transient stability analysis. Distributed computing is economically attractive providing the processing power of supercomputing at a lower cost. Several distributed software environments like the parallel virtual machine (PVM) allow an effective use of heterogeneous clusters of workstations. Both functional and domain decomposition of the transient stability problem were tested under PVM on a homogeneous cluster of eight DEC ALPHA and on an IBM SP2 machine. Functional decomposition has been obtained by the Shifted-Picard algorithm, whereas domain decomposition has been obtained concurrently running different contingencies on different nodes of the cluster, using the very dishonest Newton algorithm. In order to assess the performance of these approaches, time domain simulations, adopting detailed modeling for synchronous machines, have been carried out on a realistic-sized power network comprising 2583 buses and 511 generators.
机译:电力系统在线动态安全评估(DSA)是一个具有挑战性的计算问题。 DSA中的一个关键问题是使用在线数据每10-20分钟对大量动态稳定性意外事件进行分析。为了加快瞬态稳定性分析,已应用并行处理,并且在文献中可以找到一些结果。在本文中,作者提出了一种用于实时瞬态稳定性分析的分布式方法。分布式计算在经济上具有吸引力,它以较低的成本提供了超级计算的处理能力。诸如并行虚拟机(PVM)之类的几种分布式软件环境可以有效利用工作站的异构集群。在8个DEC ALPHA的同构群集和IBM SP2机器上的PVM下,测试了瞬态稳定性问题的功能和域分解。使用Shifted-Picard算法已获得功能分解,而使用最不诚实的牛顿算法已获得了在群集的不同节点上同时运行不同意外事件的域分解。为了评估这些方法的性能,已在包含2583总线和511发电机的实际规模的电力网络上进行了时域仿真,为同步电机采用了详细的建模。

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