Utility companies are generally well-equipped to handle routine blackouts, but often struggle with extreme weather events like hurricanes.
precipitation data
with data about an area's land use patterns—which reflect variations in power infrastructure between rural and urban areas—and population density—as an indicator of the number of transformers present—researchers are moving towards a more accurate physics-driven hurricane-induced power outageLuis Ceferino, a civil and urban engineering assistant professor and Prateek Arora, a CUE Ph.D.
Compensating for those constraints, the research team is training its model with historical outage data from Hurricanes Harvey , Michael , and Isaias . The model accounts for the nonlinear relationships between input parameters—meaning changes in one variable that do not result in proportional or consistent changes in another variable—and the likelihood of power outages.
In its ICASP 14 paper, the team focused on two key performance indices: the System Average Interruption Frequency Index and the System Average Interruption Duration Index . SAIFI measures how often customers experience power outages and SAIDI reflects the total time customers spend without power in a year. These indices are pivotal in determining the efficiency and resilience of power systems during extreme weather events.
The research team used probabilistic modeling to compute/obtain the SAIFI and SAIDI for a 10-year return period in New Jersey. It revealed that
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