Big Data Analytics on PMU Measurements
Phasor Measurement Units(PMUs)are being rapidly deployed in power grids due to their high sampling rates.PMUs offer a more current and accurate visibility of the power grids than traditional SCADA systems.However,the high sampling rates of PMUs bring in two major challenges that need to be addressed to fully benefit from these PMU measurements.On one hand,any transient events captured in the PMU measurements can negatively impact the performance of steady state analysis.On the other hand,processing the high volumes of PMU data in a timely manner poses another challenge in computation.This paper presents PDFA,a parallel detrended fluctuation analysis approach for fast detection of transient events on massive PMU measurements utilizing a computer cluster.The performance of PDFA is evaluated from the aspects of speedup,scalability and accuracy in comparison with the standalone DFA approach.
detrended fluctuation analysis (DFA),parallel computing,phasor measurement unit (PMU),event detection,Amdahl’s Law.
Mukhtaj Khan Maozhen Li Phillip Ashton Gareth Taylor Junyong Liu
School of Engineering and Design, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK School of Electrical Engineering and Information Systems, Sichuan University, 610065, China
国际会议
厦门
英文
726-730
2014-08-19（万方平台首次上网日期，不代表论文的发表时间）