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Parallel detrended fluctuation analysis for fast event detection on massive PMU data

机译：并行去趋势波动分析，可对大量PMU数据进行快速事件检测

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Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronised 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 analysed through Amdahl's Law, a revision to the law is then proposed, suggesting enhancements to its capability to analyse 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还带来了主要的计算挑战，不仅要求处理潜在的大量数据。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，以利用集群计算平台对大量PMU数据进行快速事件检测。从提速，可扩展性和准确性等方面，使用来自英国传输系统上已安装的PMU的数据对PDFA算法进行评估。首先通过阿姆达尔定律（Amdahl's Law）分析计算中PDFA的加速，然后对该定律进行修订，这表明在集群计算环境中并行化数据密集型应用程序时，其分析能力的提高了分析能力

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