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The size distributions of power outages are shown to depend on the stress, or the proximity of the load of an electrical grid to complete breakdown. Using the data for the U.S. between 2002-2017, we show that the outage statistics are dependent on the usage levels during different hours of the day and months of the year. At higher load, not only are more failures likely, but the distribution of failure sizes shifts, to favor larger events. At a finer spatial scale, different regions within the U.S. can be shown to respond differently in terms of the outage statistics to variations in the usage (load). The response, in turn, corresponds to the respective bias towards larger or smaller failures in those regions. We provide a simple model, using realistic grid topologies, which can nonetheless demonstrate biases as a function of the applied load, as in the data. Given sufficient data of small scale events, the method can be used to identify vulnerable regions in power grids prior to major blackouts.
Electric power-systems are one of the most important critical infrastructures. In recent years, they have been exposed to extreme stress due to the increasing demand, the introduction of distributed renewable energy sources, and the development of ex
The power from wind and solar exhibits a nonlinear flickering variability, which typically occurs at time scales of a few seconds. We show that high-frequency monitoring of such renewable powers enables us to detect a transition, controlled by the fi
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The conventional outage in wireless communication systems is caused by the deterioration of the wireless communication link, i.e., the received signal power is less than the minimum received signal power. Is there a possibility that the outage occurs
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