Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 6, Issue 1, Pages 360-368Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2014.2340446
Keywords
Amdahl's law; detrended fluctuation analysis (DFA); event detection; Hadoop; MapReduce; openPDC; parallel computing; phasor measurement unit (PMU); wide area monitoring systems (WAMS)
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Funding
- U.K. Engineering and Physical Sciences Research Council (EPSRC) [EP/K006487/1]
- National Grid, U.K
- Engineering and Physical Sciences Research Council [EP/K006487/1] Funding Source: researchfish
- The British Council [GII106] Funding Source: researchfish
- EPSRC [EP/K006487/1] Funding Source: UKRI
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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.
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