Volume 126, Number 4, May 2019
|Number of page(s)||7|
|Section||Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics|
|Published online||26 June 2019|
Load dependence of power outage statistics
1 Max Planck Institute for Dynamics and Self - Organization - Am Fassberg 77, 37077 Göttingen, Germany
2 WW8- Materials Simulation, Department of Materials Science, Friedrich-Alexander-Universität Erlangen-Nürnberg Dr.-Mack-Str. 77, 90762 Fürth, Germany
3 School of Science and Technology, Nottingham Trent University - Clifton Lane, Nottingham NG11 8NS, UK
(a) Present address: Physics Department, SRM University - AP-Amaravati, India;; email@example.com
Received: 11 February 2019
Accepted: 20 May 2019
Dynamics of power outages remain an unpredictable hazard in spite of expensive consequences. While the operations of the components of power grids are well understood, the emergent complexity due to their interconnections gives rise to intermittent outages, and power-law statistics. Here we demonstrate that there are additional patterns in the outage size distributions that indicate the proximity of a grid to a catastrophic failure point. Specifically, the analysis of the data for the U.S. between 2002 and 2017 shows a significant anti-correlation between the exponent value of the power-law outage size distribution and the load carried by the grid. The observation is surprisingly similar to dependences noted for failure dynamics in other multi-component complex systems such as sheared granulates and earthquakes, albeit under much different physical conditions. This inspires a generic threshold-activated model, simulated in realistic network topologies, which can successfully reproduce the exponent variation in a similar range. Given sufficient data, the methods proposed here can be used to indicate proximity to failure points and forecast probabilities of major blackouts with a non-intrusive measurement of intermittent grid outages.
PACS: 45.70.Ht – Avalanches / 89.75.Da – Systems obeying scaling laws / 64.60.aq – Networks
© EPLA, 2019
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