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Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

机译：用于大规模PMU数据的快速事件检测的并行去趋势波动分析

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摘要

Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl’s Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment.
机译：相量测量单元（PMU）由于其高采样率和同步测量而正在迅速部署在电网中。除了围绕数据存储的新问题外，这些设备的高数据报告率对处理潜在的大量数据提出了重大的计算挑战。电力系统领域现在需要能够处理大量数据的快速算法。本文提出了一种新颖的并行去趋势波动分析（PDFA）方法，利用集群计算平台，可以对大量PMU数据进行快速事件检测。使用来自英国传输系统上已安装的PMU的数据，从加速，可伸缩性和准确性方面评估PDFA算法。首先通过阿姆达尔定律分析PDFA在计算中的速度。然后提出对该法律的修订，以增强其在集群计算环境中并行化数据密集型应用程序时分析计算性能增益的能力。

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