两电平并联暂态稳定约束最优潮流性能分析

Guangchao Geng, Q. Jiang, V. Ajjarapu
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引用次数: 2

摘要

暂态稳定约束最优潮流(TSCOPF)是电力系统中计算量最大的应用之一。利用高性能计算(high performance computing, HPC)技术并行化和加速TSCOPF求解过程已经取得了一定的研究成果,但在Beowulf集群等高性能计算平台上,如何通过性能分析识别瓶颈并提高求解效率仍然存在挑战。本文基于前两位作者的研究成果[8,12]——基于缩减空间内点法(RIPM)的两级并行TSCOPF,提出了一种系统的性能分析方法。为了发现性能瓶颈,采用了全面的性能分析程序——超时分析、MPI/OpenMP分析和跟踪。采用最先进的性能分析软件生成和可视化性能数据,为并行性能增强提供指导。在一个2746总线系统上的数值结果表明,该方法在求解贝奥武夫集群上的大规模多事件TSCOPF时是有效的,并且开销相对较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance analysis for two-level parallel transient stability constrained optimal power flow
Transient stability constrained optimal power flow (TSCOPF) is one the most computational-intensive applications in power systems. Research efforts were made to utilize high performance computing (HPC) technology to parallelize and accelerate TSCOPF solving process, but challenges still exist in performance analysis to identify bottlenecks and improve efficiency on practical HPC platforms such as Beowulf clusters. Based on first two authors' previous work [8, 12] - two-level parallel TSCOPF with reduced-space interior point method (RIPM), a systematic performance analysis approach is demonstrated in this paper. Comprehensive performance analysis procedures - wall time analysis, MPI/OpenMP profiling and tracing - is employed in order to discover performance bottlenecks. State-of-the-art performance analysis software are employed to generate and visualize performance data, providing guidelines for parallel performance enhancement. Numerical results on a 2746-bus system show effectiveness of the proposed approach and relative low overhead in solving large-scale multi-contingency TSCOPF on a Beowulf cluster.
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