A data fusion probabilistic model for hurricane-induced outages in electric power grids Mensah, Akwasi F.; Dueñas-Osorio, Leonardo
Prediction of outages in electric power systems before a hurricane can be enhanced by exploiting data not typically used for such purposes, including system contingencies during normal operation. This data-based enhancement is necessary to inform disaster planning and preparedness, as well as to speed up the restoration of the system while capturing local trends. This paper presents a framework that integrates hurricane-induced outage predictions based on component fragilities, physics of power flows, and network responses, along with increasingly available spatio-temporal information of daily outages, so as to better assess outage risks in the electric system. The study adapts Linearly Constrained Least Squares (LCLS) by making space the dependent variable instead of time (as traditionally used), and then determine an optimal linear fusion of the predicted outages with information from the daily outage trackers for enhanced outage distribution assessment. Using the electric power system as an illustrative example, the study shows how data fusion improves the prediction accuracy by including daily outage information, which implicitly contains the spatial structure of the network as well as the current physical state of its components and interactions, including ageing, fatigue and recent hardening or embedding of smart grid technologies. The fusion-based framework predicts an overall outage of 84% in the county under Hurricane Ike winds, which strongly agrees with the reported outage of 86% by the utility provider in the aftermath of the event as opposed to overall outage of 90% predicted without data fusion.
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