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  • 标题:Revealing Novel Connections Between Space Weather and the Power Grid: Network Analysis of Ground-Based Magnetometer and Geomagnetically Induced Currents (GIC) Measurements
  • 本地全文:下载
  • 作者:Joseph Hughes ; Ryan Mcgranaghan ; Adam C.Kellerman
  • 期刊名称:Space Weather
  • 印刷版ISSN:1542-7390
  • 出版年度:2022
  • 卷号:20
  • 期号:2
  • 页码:1-16
  • DOI:10.1029/2021SW002727
  • 语种:English
  • 出版社:American Geophysical Union
  • 摘要:The growing depth and breadth of data spanning the solar-terrestrial environment requires new ways of representing and analyzing the available information. This paper applies one such new data representation—network analysis—to the study of Geomagnetically Induced Currents (GICs) in electric power lines. This work uses newly available electric current data collected by power utilities through the the Electric Power Research Institute (EPRI) SUNBURST project and magnetometer data from the Super Magnetometer Initiative. The magnetometer data are analyzed using wavelet analysis. This new analysis method shows deviations to be more likely for equatorial stations close to water, which may be caused by the coast effect. The deviation likelihood is a complex function of latitude and magnetic local time. The GIC data are analyzed using “Quiet Day Curves” (QDCs) which help isolate geomagnetic disturbances. We find that current deviations are more common in the early morning sector, but this trend differs from station to station. These current and magnetometer data are represented in a network as nodes which are connected when both the current and magnetic measurements have a statistically significant deviation from their baseline behavior. This network is used to study the link between space weather and GICs. To do this, times when a current deviation exists are compared to times when magnetic deviations exist for each magnetometer - current sensor pair. Current deviations are, on average, 1.83 times more likely when there are magnetic deviations. However, some magnetometer deviations are more indicative than others, with the strongest probability multipliers reaching 3.
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